BOX 1-1
Biological and Biotechnology Sciences Competency

The biological and biotechnology sciences competency is focused on investigating the fundamental sciences of biology, biological systems, and biomaterials, with the goal of enabling transformational Army capabilities. The competency is immersed in a rapidly expanding field, encompassing foundational biological research as well as the growing fields of engineering with biology and engineering biological systems.^a

Within the competency are three core competencies: biology in military environments, biosynthesis and biomaterials, and synthetic biology tools. The biology in military environments core competency focuses on discovering, understanding, and controlling microbiomes that are believed to be relevant to military applications (what the competency describes as “military material”).^b The biosynthesis and biomaterials core competency focuses on bio-derived materials—specifically their production, characterization, and processing for operational use.^c The synthetic biology tools core competency focuses on developing novel genetic engineering capabilities to harness military relevant chassis.^d

Biology in Military Environments Core Competency

The biology in military environments core competency intramural team’s progress since its new and expanded focus beginning in 2018 is highly impressive and commendable. Under excellent Army Research Laboratory (ARL) leadership from the competency chief, it has developed a strong portfolio supported by state-of-the-art equipment and capable staff. Good progress is being made on standardization of multiple phyla cloning and genome manipulation. The core competency study of complex natural communities and complex biological processes, including the function of complex microbial communities, reflects an understanding of the state of the art. Additionally, the competency goals with respect to plastics degradation are aligned with the state of the art in the field and reflect an understanding of the underlying science and research conducted elsewhere, and ARL’s research methods associated with plastic degradation are consistent with standards in the field.

ARL is positioned to become a leader in plastic degradation. Current intramural efforts at ARL to develop standardized methods to characterize and screen for polyurethane degradation, and high-throughput assays for plastics degradation have the potential to set the standard for the field and accelerate discovery and engineering of relevant platforms for polyurethane degradation. Additionally, ARL’s successful extramural efforts on novel chassis for polyurethane degradation may lead to novel enzymes and relevant degradation rates at scale. Continued growth and success in these areas promise to tackle a global challenge.

Within the core competency, the strongest areas of expertise among extramural scientists were those working in molecular genetics and with complex microbial systems studies, and those performing exquisite analytical instrumentation for field measurements. In broadly viewing the portfolio, it was found that extramural research might benefit from expansion into research themes that align with ongoing research in the field (described in Chapter 3), and from bringing additional research laboratories into the extramural research portfolio if they are not already doing so, through newer collaborative agreements. The strongest areas of expertise within the intramural teams were in molecular biology and synthetic biology. There is a need to consult with or recruit experts in the specific areas of fungal biology, complex microbial community research, bioinformatics, and artificial intelligence (AI) and machine learning (ML). The computational infrastructure within the ARL portfolio could serve as a remarkable resource if the core competency could better access and capitalize on it.

The toured research facilities at ARL’s Adelphi Laboratory Center are more than adequate to carry out the proposed research and hold potential for additional research endeavors Concerning ARL’s exploratory biology research thrust, the research portfolio focusing on extramural exploratory biology is exciting, impressive, and expansive. Elements of the research explore fundamental outstanding questions in biology that could significantly impact both expanding the understanding of basic biology and contribute to the development of new biological tools for applicative use. A weakness of the exploratory biology portfolio was the eukaryotic biology section. This section included a focus on systems that seemed disconnected or outdated, and the feasibility of some of the proposed research was questionable (see Chapter 3 for more details). Moving forward, there is a large opportunity for the

exploratory biology thrust area to expand the portfolio on microbial community work. The current challenge in dissecting metabolic networks of large communities is the ability to do this in more complex communities containing much greater biodiversity. For example, cutting-edge research on complex metabolic interactions focuses on communities that are either more representative of natural communities or of pre-existing environmental biofilms, which remains challenging as these systems are often unculturable.

There is also room for expansion into exploring the spatial organization of complex microbial communities. Understanding the spatial distribution of individual cells and how individuals localize throughout communities will provide insight into the physical and chemical interactions and how these interactions contribute to biofilm growth, stagnation, or death. There is also an opportunity to expand into the study of diverse biological species. The current portfolio contains projects on a few different species, but it is largely limited to bacteria and does not appear to consider the broad diversity that exists in nature including archaea, fungi, algae, protists, and phages. For a comprehensive study of complex microbes, more research focusing on these and other diverse species could be incorporated.

Identified opportunities include the following:

• To connect with leading edge research and researchers, ARL could (1) enhance attendance at relevant conferences (in particular, Gordon Research Conferences [GRCs]) and (2) create an internship program for intramural scientists to spend some in external laboratories, as well as reciprocal arrangements for external researchers to spend time at ARL intramural laboratories.
• It is important in microbial community thrusts to recognize that archaea and phage are part of complex communities, and bioinformatics tools can now follow these members of complex communities. Similarly, ARL may want to apply greater consideration to protists because they can play a vital role in the function of microbial communities.^e
• Across the core competency, modern computational biology, bioinformatics, and AI approaches are not yet being fully harnessed. Incorporating these approaches is imperative at every level. Please see the Biological and Biotechnology Sciences chapter for a deeper conversation regarding how these elements can be incorporated.
• The core competency understands that the state of the art is to consider complex natural communities while recognizing the challenges associated with their study. There is a similar goal running through the synthetic biology tools core competency, which looks at novel chassis and genetic circuits and parts. A drive toward efficiency and improved outcomes requires collaboration across these core competencies so that truly interchangeable protocols and parts are developed.
• Current intramural efforts at ARL to develop standardized, high-throughput assays for plastics degradation have the potential to set the standard for the field and accelerate discovery and engineering of relevant platforms for polyurethane degradation. Historical models for this are the National Renewable Energy Laboratory’s standard assays for biomass characterization and degradation^f and Idaho National Laboratory’s well-characterized feedstock library.^g
• ARL successes include the extramural efforts on novel chassis for polyurethane degradation that may lead to novel enzymes and relevant degradation rates at scale. The Department of Energy (DOE) has recently invested significant resources into plastics degradation, and thus some interagency coordination could reduce the potential of duplicated efforts. Still, ARL’s focus on polyurethane is a unique niche from more “popular” work on polyethylene terephthalate (PET), nylon, polyethylene, and polypropylene and represents a gap where ARL can lead.
• ARL can provide integration of the core competency’s intramural and extramural research efforts by providing a platform for each entity to network more directly with the other—perhaps through onsite symposia, reciprocal internships, joint posters at meetings, or other opportunities.

• Many areas of research across different core competencies and the broader competency could benefit from increased communication and collaboration between core competencies and between competencies. For example, the microfluidics work could be employed in research concerning microbial growth. Likewise, molecular dynamics simulations and modeling can be incorporated to provide powerful insight into complex biological systems. Enhancing cross-pollination can be achieved through annual symposiums or mini-conferences held specifically within and for ARL intramural researchers.
• There is substantial technical overlap between the research being conducted in-house and in other government laboratories, both within the Department of Defense and other agencies. For example, the National Aeronautics and Space Administration (NASA) has similar research interests in life in extreme environments, slow growth, etc. ARL could consider connecting to these entities to explore collaborations. This could be initiated through a workshop. Search tools that can help the competency determine the work going on in other agencies include the National Institutes of Health (NIH) “RePORT”^h and National Science Foundation (NSF)-funded award search tools.ⁱ DOE grants are also searchable in the Portfolio Award and Management System.^j
• There is a missed opportunity for more discovery-based work that can identify novel/unknown enzyme classes from polyurethane-degrading isolates that can be developed by the intramural researchers and also establish the extramural branch of ARL as leaders in this space. Potential strategies that can enable this include proteomics to identify secreted enzymes that degrade plastics, materials characterization to confirm the extent and types of chemical degradation, and expanded data science/bioinformatics support to both mine and manage generated data sets.
• Current ARL efforts focused on rational cutinase engineering are duplicative. While applied here to polyurethane degradation, the enzymatic activity being optimized is similar to that in other polymers such as PET. The literature is extensive, with recent examples leveraging AI to enhance activity by several orders of magnitude to industrially relevant rates rather than the 2-fold observed in the presented ARL research. Moreover, cutinase/polyesterase activity is a more or less “solved” problem (although there are still unknown aspects of both of these enzyme groups and activities, including chitin lytic polysaccharide monooxygenases [LPMOs]). The challenge for polyurethane is the carbamate bond, where discovery is still needed. ARL may consider conversations with leaders, such as those in the references below (e.g., researchers such as Craig Criddle), who may help define future directions:
  1. Lu et al., 2022.^k
  2. Acero et al., 2013.^l
  3. Stepnov et al., 2024.^m
  4. Wu and Criddle, 2021.ⁿ
• Concerning the extramural work on slow growth microbiology, microbial metabolism in gypsum halite crusts has long been known,^o but the work on iron utilization could be of interest if magnetite is present.
• ARL’s interest on the effects of melting of the permafrost is timely, and Waldrop et al. (2023)^p gives an idea who funds microbial work for permafrost. A unique niche would be to advance the basic science in the field (e.g., characterize the native ecosystem). Interagency coordination may be helpful; for example, the use of NASA satellite data and other ground studies, and working with NIH for its interest in the release of disease vectors. NSF, DOE, and the U.S. Geological Survey may have an interest as well.
• The idea of using bacteria to sequester precious elements as a future direction is valuable. Note some of this is happening already (e.g., uranium oxidation; lanthanide recycling, see Rogiers et al. [2022] review;^q Good et al. [2024];^r and other work from Martinez-Gomez laboratory) as there may be an opportunity to reach out for additional extramural partners.

• There is significant potential in the Breakdown of Polymeric Materials Using Synthetic Biology Approaches project to identify new and diverse enzymes and metabolic pathways for downstream plastic-degrading functions. Bacterial and fungal geneticists and molecular biologists could provide expertise on methods for identifying and characterizing novel enzymes. Additionally, in the pursuit of improved amidases and cutinases, approaches such as deep mutational scanning or in vitro evolution could be considered.

Biosynthesis and Biomaterials Core Competency

The biosynthesis and biomaterials core competency highlighted several projects that are on par with other research institutions nationally and internationally and show a strong understanding of research done elsewhere. The presentations did an excellent job building on well-established approaches for the development of melanin, magnetosome, and silk applications. ARL’s work on novel biosynthesis of non-peptide linkages and unnatural amino acids in cell-free systems is pioneering and cutting-edge. Its work on traditional self-assembling biomaterials, novel chemistry, and protein capture shows that the researchers have a broad understanding of science and research conducted elsewhere. This work is well-connected to leading laboratories in the field and building from state-of-the-art scientific endeavors. The laboratories selected are excellent nodes in the broader cutting-edge scientific community, and the internal pipelines for discovery represent well-thought-out deployment of established tools and techniques to enable rapid response. Additionally, the capabilities around high-performance computing (HPC) demonstrate a strong understanding and capability in well-established legacy systems. These are being utilized to the best of their limitations and are an important foundational infrastructure necessary for building next-generation data science capabilities. The research methods around self-assembling biomaterials, novel chemistry, and protein capture are also sound and represent a thorough understanding of the quantitative metrics, experimental design, and traditional modeling approaches. Such work reflects high-performing teams with excellent capabilities to bring state-of-the-art biotechnology into the ARL ecosystem.

Concerning its fungal research, ARL has rightly chosen melanins as one of its focused application efforts, as these polymers present a plethora of useful properties ranging from protection from ultraviolet radiation, enzymatic lysis, temperature extremes, and oxidative damage. ARL’s progress on isolation of fungal melanin and its conversion into useful materials, such as graphitic carbon, is impressive. ARL has made incredible progress toward its goals. It is working toward the state of the art in synthetic biology and is currently the state of the art in melanin production.

Across the core competency, the teams evinced solid scientific expertise in the areas of the well-established fields of biological chemistry, materials science, and computational chemistry. The work on demonstrating the role of biology in templating materials, developing novel bio-orthogonal chemistry, and deploying pipelines is connecting to well-established tools that enable rapid response. To move into emerging scientific opportunities, it will be important to diversify the expertise, bringing on leaders in the fields of supramolecular polymers, protein design, and modern data science.

Finally, the facilities and resources are adequate to support current endeavors in the biosynthesis and biomaterials core competency. As in-house data science capabilities are being developed, it will be important to bring in modern HPC capabilities (i.e., graphics processing unit-based clusters).

Identified opportunities include the following:

• A modern data science strategy is a framework and approach to managing data that puts AI in the center. This implies that efforts to benchmark, collect and process data should focus on making such data accessible to transformer-based AI methodologies. Unique to this space are practices for meta-data collection that connect scientific efforts traditionally considered to be totally independent, as AI can help to identify novel and non-obvious relationships that may catalyze new areas for scientific exploration or support existing scientific efforts, and/or catalyze cross-cutting collaborations with other core competencies and competencies. Current core competency projects either did not utilize AI or considered legacy ML approaches as an afterthought. To keep at pace with the field, dedicated intramural staff and extramural efforts will need to focus on leveraging these important technologies.

• While the core competency is employing sound research methods, the methods used to evaluate biomanufacturing lacked the industry standard quantitative assessments critical in understanding gaps from current methods to commercial viability. All future biomanufacturing work will need to develop a clear data science strategy with key quantitative metrics recorded for changes around environmental and genetic impact on titer (g/L), rate (g/L/hr), and yield (g/g). Additional factors around the purity of products, cell density, genetics, environment, and additional metadata will need to be recorded. See Konzock and Nielsen (2024),^s which provides key metrics. Increasing collaborations with external biomanufacturing partners could further enhance alignment with industry standards, including those standards focused on scalability and purity.
• Leaders in the data science field—such as Ilias Tagkopoulos, who is director of NSF’s and the U.S. Department of Agriculture’s AI Institute for Next Generation Food Systems, and Steven Brown, who is coordinating 25 AI centers funded by NSF^t—may provide guidance to ARL for creating a clear data strategy.
• Closer integration with the synthetic biology tools core competency would benefit all projects because it will increase throughput and standardization of data generation to improve interoperability and robustness. For projects such as Biomanufactured Cellulose as a Versatile Feedstock for Military Applications, the integration with the synthetic biology capability may be essential, given the inherent limitations of the natural organism evolutionary constraints.
• Expansion of the toolkit in biology and novel biomaterials, and a greater diversification of biomaterials being studied, is encouraged. Scientists can now create component parts, and it will be important to understand what products and materials can be made that were not previously accessible.
• Supramolecular polymers are promising areas of research in the biomaterials field, and the integration of covalent and supramolecular polymers will benefit not just biomaterials but also the critical field of sustainable materials, including biodegradation and recycling. Supramolecular polymers introduce the opportunity of non-covalent bonding among monomers and thus their easy reuse.^u They also introduce opportunities for dynamic materials for robotic applications.^v,w
• Attending GRC, such as the Peptide Materials GRC and the Supramolecular Chemistry GRC, will help in understanding and developing this new opportunity.
• There are opportunities to understand what the limitations of templating are in using cellular systems by connecting with the synthetic biology capabilities in ARL to control biological morphologies and compositions. Further mechanisms of templating need to be explored, such as those recently demonstrated by the Baker laboratory.^x
• ARL could expand into recently developed approaches enabled by the advent of synthetic biology and AI in the area of protein capture agents—specifically “minibinders” and “nanocages,” pioneered by the Baker and King laboratories at the University of Washington,^y which are now being commercially deployed. There is an opportunity to expand the capture agents to move beyond traditional methods and adopt modern AI-driven approaches. ARL could connect with the Baker laboratory to understand new approaches for designing protein binders. Leveraging these methods may create more capabilities for manufacturing viability, address identified challenges, and better determine efficacy in complex biological environments.
• The core competency could better explore the broader community by connecting with RosettaCommons, which is the foremost leading scientific community around AI-driven biotechnology and may provide knowledge on recent advances in protein and peptide science.^z Another resource could be with DeepMind, developers of AlphaFold.^aa

• Innovation in biomanufacturing is being invested through a collaboration between NSF and DOE’s Bioenergy Technology Office, which is being funded by the Agile BioFoundry. Connecting with these groups working on protein production could help ARL understand advanced technologies in this area.
• A promising new area of research is food biotechnology.^bb Academic domain experts such as Bruce German (director of Foods for Health Institute) and industry leaders, such as Harold Schmitz (The March Group, former chief strategy officer of Mars, Inc.), Victor Friedberg (founder of New Epoch, S2G, FoodShot), and Meati Foods founder Justin Whiteley may be helpful to connect with. Furthermore, recent organizations have been launched to bring food into the modern biotechnology world through the Periodic Table of Foods.^cc Discussion with leaders here (e.g., Selena Ahmed^dd) could be transformative.
• Biological systems can create novel component parts by leveraging abiotic chemistries, and exploration could be initiated to understand the scope of abiotic chemistries that can be developed to create component parts and to understand what materials and products can be made using these novel component parts. The synthetic biology tools core competency could work with the biosynthesis and biomaterials core competency toward this goal.
• Melanin is a polymeric natural products that can be derived from polyketide dihydroxynaphthalene (DHN) melanin pathways in fungi. This pathway is amenable to molecular genetics to produce variations in melanin polymers and melanin colors. This calls for better linkages between synthetic biology approaches in the synthetic biology tools core competency with material science research in the biosynthesis and biomaterials core competency.
• Other useful fungal biomaterials for future consideration are hydrophobins, which make excellent surfactants and are used in nanotechnology.^ee–gg
• Most fungi contain numerous hydrophobin genes, and Luciano-Rosario and colleagues illustrate a technology to recycle a marker gene to delete a 7 gene hydrophobin family.^hh

Synthetic Biology Tools Core Competency

ARL’s portfolio of extramural and intramural projects in the synthetic biology tools core competency addresses highly scientifically relevant and high-impact questions in the field. There is a growing recognition within the field that microbiomes and diverse non-model chassis will be pivotal to providing scalable solutions to emerging challenges (both grand civilian societal ones and military), and thus ARL’s focus in these areas are aptly chosen. Accomplishments from the competency have begun to mature into scalable bioproduction efforts (e.g., biomagnets) and ARL’s recent sprint to onboard 10 novel organisms in 10 weeks is laudable as among the first in the field. The success of this core competency can be attributed in part to a robust extramural program that currently funds multiple recognized leaders in synthetic biology research via the Army Center for Synthetic Biology. They are doing innovative foundational work in genetic tool development, microbiome engineering, and biomaterials. Such investments promise to lead the field. Additionally, the speed with which the ARL research teams have pivoted to work with filamentous fungi is impressive. They have made significant progress in a relatively short time. Their establishment of high throughput methods is state of the art. The competency has an understanding of the science being performed in the field at large, and they are addressing cutting-edge questions and seeking out novel niches—for example, their microbiome work is focused on material synthesis rather than more common small molecule chemicals. They are also using research methods and methodologies that are sound and appropriate.

The qualifications of the teams supporting the synthetic biology tools core competency to meet existing goals are adequate; however, there needs to be more bioinformatics, AI, and data science support. Data science personnel are needed to build the requisite infrastructure to support modern AI, and foundational models and additional bioinformatics support (personnel) is required to effectively use these tools. It is also recommended that multiple ARL team members acquire additional expertise in molecular biology and the physiology of filamentous fungi by aligning with experienced mycologists

with extensive years of experience with different genera (suggestions of who to connect with are provided in Chapter 3). This expertise could be grown through 2–3 month (ideally 6 months)–long visits to the laboratories of experts in the field, or such experts could be hosted at ARL (this would be less ideal, since visiting a laboratory would offer access to more demonstrations and more fungal experts).

The facilities appear more than adequate to perform the proposed work. The close proximity of autoclaves, deoxyribonucleic acid and ribonucleic acid sequencing instruments, fluorescent microscopes, etc., is ideal for rapid progress on multiple fronts. ARL operates in Biosafety Level 2 facilities, which allows for exploration as researchers develop their portfolio related to fungi. ARL is also building a biofoundry for non-model chassis microbes and a (distributed) facility that combines biology, automation, AI, synthetic biology, materials science, and chemistry to rapidly prototype and test engineered biological systems. ARL may consider leveraging data science expertise in the biofoundary’s development.

Identified opportunities include the following:

• If it is not already doing so, ARL may want to expand its extramural collaborations to include more up-and-coming researchers.
• The portfolio presented to the panel by the extramural and intramural teams does not address the need for biocontainment or engineering of released microbes that is implied by that effort. If work is not being performed in this area, ARL could expand its efforts here.
• Enhancing research efforts on biological materials through its extramural collaborations could be helpful. There are opportunities to look deeper at conductance (the ease of how electric currents move through materials), optically active materials, patterned materials, and control interactions with the electromagnetic spectrum.
• Team members could attend the biannual GRC or the Fungal Genetics Conference to keep abreast of current technological approaches for filamentous fungi.
• Box 3-1 in Chapter 3 contains many references and resources that could enable ARL to develop its fungal research focus.

__________________

^a Passages of this paragraph from V. Martindale and J. Sumner, 2024, “Biological and Biotechnology Sciences Technical Advisory Board—Story of the Competency,” DEVCOM ARL presentation to the committee, July 9.

^b B. Adams, 2024, “Synthetic Biology and Biology in Military Environments,” DEVCOM ARL presentation to the committee, July 9.

^c Coppock, 2024, “Biosynthesis and Materials Overview,” DEVCOM ARL presentation to the committee, July 10.

^d Passages from B. Adams, 2024, “Synthetic Biology and Biology in Military Environments,” DEVCOM ARL presentation to the committee, July 9.

^e S. Guo, W. Xiong, X. Hang, Z. Gao, Z. Jiao, H. Liu, Y. Mo, et al., 2021, “Protists as Main Indicators and Determinants of Plant Performance,” Microbiome 9:64.

^f NREL Bioenergy, “Biomass Compositional Analysis Laboratory Procedures,” https://www.nrel.gov/bioenergy/biomass-compositional-analysis.html, accessed October 15, 2024.

^g Idaho National Laboratory, “Bioenergy Feedstock Library,” Biomass Feedstock National User Facility, https://bioenergylibrary.inl.gov/Home/Home.aspx, accessed October 15, 2024.

^h National Institutes of Health, “Welcome to Research Portfolio Online Reporting Tools (RePORT),” https://report.nih.gov, accessed October 15, 2024.

ⁱ National Science Foundation, “Awards Simple Search,” https://www.nsf.gov/awardsearch, accessed October 15, 2024.

^j Department of Energy, “Portfolio Analysis and Management System (PAMS),” https://www.energy.gov/science/portfolio-analysis-and-management-system-pams, accessed October 15, 2024.

^k H. Lu, D.J. Diaz, N.J. Czarnecki, C. Zhu, W. Kim, R. Shroff, D.J. Acosta, et al., 2022, “Machine Learning-Aided Engineering of Hydrolases for PET Depolymerization,” Nature 604:662–667.

^l E.H. Acero, D. Ribitsch, A. Dellacher, S. Zitzenbacher, A. Marold, G. Steinkellner, K. Gruber, et al., 2013, “Surface Engineering of a Cutinase from Thermobifida Cellulosilytica for Improved Polyester Hydrolysis,” Biotechnology and Bioengineering 110:2581–2590.

^m A.A. Stepnov, E. Lopez-Tavera, R. Klauer, C.L. Lincoln, R.R. Chowreddy, G.T. Beckham, V.G.H. Eijsink, et al., 2024, “Revisiting the Activity of Two Poly(Vinyl Chloride)- and Polyethylene-Degrading Enzymes,” Nature Communications 15:8501.

ⁿ W.-M. Wu and C.S. Criddle, 2021, “Chapter Five—Characterization of Biodegradation of Plastics in Insect Larvae,” in Methods in Enzymology, Vol. 648.

^o See, for example, L.J. Rothschild, L.J. Giver, M.R. White, and R.L. Mancinelli, 1994, “Metabolic Activity of Microorganisms in Evaporites,” Journal of Phycology 30:431–438, https://doi.org/10.1111/j.00223646.1994.00431.x.

^p M.P. Waldrop, C.L. Chabot, S. Liebner, S. Holm, M.W. Snyder, M. Dillon, S.R. Dudgeon, et al., 2023, “Permafrost Microbial Communities and Functional Genes Are Structured by Latitudinal and Soil Geochemical Gradients,” The ISME Journal 17:1224–1235.

^q T. Rogiers, R. van Houdt, A. Williamson, N. Leys, N. Boon, and K. Mijnendonckx, 2022, “Molecular Mechanisms Underlying Bacterial Uranium Resistance,” Frontiers in Microbiology 13:822197.

^r N.M. Good, C.S. Kang-Yun, M.Z. Su, A.M. Zytnick, C.C. Barber, H.N. Vu, J.M. Grace, et al., 2023, “Scalable and Consolidated Microbial Platform for Rare Earth Element Leaching and Recovery from Waste Sources,” Environmental Science and Technology 58:570–579.

^s O. Konzock and J. Nielsen, 2024, “TRYing to Evaluate Production Costs in Microbial Biotechnology,” Trends in Biotechnology 42(11):1339–1347. https://doi.org/10.1016/j.tibtech.2024.04.007.

^t See AI Institutes Virtual Organization, “AI Institutes Virtual Organization,” https://aiinstitutes.org/about-aivo, accessed October 15, 2024.

^u T. Aida, E.W. Meijer, and S. Stupp, 2012, “Functional Supramolecular Polymers,” Science 335:813–817.

^v C. Li, A. Iscen, H. Sai, K. Sato, N.A. Sather, S.M. Chin, Z. Álvarez, L.C. Palmer, G.C. Schatz, and S.I. Stupp, 2020, “Supramolecular-Covalent Hybrid Polymers for Light-Activated Mechanical Actuation,” Nature Materials 19:900–909.

^w S.D. Cezan, C. Li, J. Kupferberg, L. Dordevic, A. Aggarwal, L.C. Palmer, M.O. de la Cruz, and S.I. Stupp, 2024, “Fast Photoactuation Driven by Supramolecular Polymers Integrated into Covalent Networks,” Advanced Functional Materials 34(49):2400386, https://doi.org/10.1002/adfm.202400386.

^x A. Saragovi, H. Pyles, P. Kwon, N. Hanikel, F.A. Dávila-Hernández, A.K. Bera, A. Kang, et al., 2024, “Controlling Semiconductor Growth with Structured De Novo Protein Interfaces,” bioRxiv, https://doi.org/10.1101/2024.06.24.600095.

^y Baker Laboratory, “Publications,” https://www.bakerlab.org/publications, accessed October 15, 2024.

^z Rosetta Commons, “Rosetta Commons,” https://www.rosettacommons.org/about, accessed October 15, 2024.

^aa AlphaFold, “Overview,” https://deepmind.google/technologies/alphafold, accessed October 15, 2024.

^bb AgFunder, “AgFunder Global AgriFoodTech Investment Report 2024,” https://agfunder.com/research/agfunder-global-agrifoodtech-investment-report-2024, accessed October 15, 2024.

^cc The Periodic Table of Food Initiative, “Mapping Food Quality to Improve Human and Planetary Health,” https://foodperiodictable.org, accessed October 15, 2024.

^dd The Periodic Table of Food Initiative, “Selena Ahmed,” https://foodperiodictable.org/bio/selena-ahmed, accessed October 15, 2024.

^ee V. Lo, J.I.-C. Lai, and M. Sunde, 2019, “Fungan Hydrophobins and Their Self-Assembly into Functional Nanomaterials,” Advances in Experimental Medicine and Biology 1174:161–185, https://doi.org/10.1007/978-98113-9791-2_5.

^ff T. Tanaka, Y. Terauchi, A. Yoshimi, and K. Abe, 2022, “Apergillus Hydrophobins: Physicochemical Properties, Biochemical Properties, and Functions in Solid Polymer Degradation,” Microorganisms 10(8):1498.

^gg H. Fan, B. Wang, Y. Zhang, Y. Zhu, B. Song, H. Xu, Y. Zhai, M. Qiao, and F. Sun, 2021, “A Cryo-Electron Microscopy Support Film Formed by 2D Crystals of Hydrophobin HFBI,” Nature Communications 12:7257.

^hh D. Luciano-Rosario, J.L. Eagan, N. Arylal, E.G. Dominguez, C.M. Hull, and N.P. Keller, 2022, “The HydrophobinGene Family Confers a Fitness Trade-Off Between Spore Dispersal and Host Colonization in Penicillium expansum,” mBio 13:e0275422.

BOX 1-2
Network, Cyber, and Computational Sciences Competency

The network, cyber, and computational sciences competency focuses on distributed, resilient, secure networking and resource-adaptive decentralized computing for decision dominance. Within the competency are three core competencies: cyber defense and cybersecurity, computational methods for modeling and learning, and resilient and adaptive communication networks.^a The computational methods for modeling and learning core competency focuses on the development of mathematical algorithms, deterministic and stochastic models, multi-scale methods, and uncertainty quantification to simulate complex, physical systems to understanding variability, predicting system evolution with quantified confidence, and enabling exploitation of those predictions in both high-performance and limited-compute settings. It also focuses on developing and leveraging deep learning techniques to tackle an ever-expanding array of sensing applications involving acoustic, electromagnetic, radio frequency, and optical modalities. The cyber defense and cybersecurity core competency focuses on theories, models, optimized algorithms, and experimentation for prevention, detection, mitigation, monitoring, and prediction of adversarial activities and their impacts within cyberspace. It also focuses on the development of cyber deception and counter-deception strategies to provide resilience and defenses against adaptive and sophisticated adversaries. Its scope includes traditional enterprise-level and tactical information networks as well as non-traditional networks such as communication buses found on vehicle platforms. The resilient and adaptive communication networks core competency focuses on theories, methods, algorithms, and experimental approaches to enable resilient communications in complex and contested environments via novel communication modalities, multilayer adaptive protocols for robust information delivery (including storage, computing, and communications), emerging quantum networks, interpretable and adversarial machine learning (ML) to enable autonomous control of heterogeneous network structures, and dynamics for resilience to adversarial attacks.

Computational Methods for Modeling and Learning Core Competency

The assessment criteria asked for commentary concerning whether the work within the computational methods for modeling and learning core competency is at par with other scientific organizations nationally and internationally. While individual research areas within the core competency have been met with success, a challenge to assessing the overall quality of the core competency portfolio was that the projects appear to be more of a collection of results sown across different areas of the computational mathematics field without a coherent, scientifically focused framework. As a result, scientific topics did not seem to connect or cross-fertilize in meaningful ways. There is an opportunity for the core competency’s priority scientific questions (PSQs), which are currently more project-specific and narrow, to be leveraged toward creating a broader and more unified scientific framework for the core competency that reflects cutting-edge scientific challenges in computational mathematics in line with the Army Research Laboratory’s (ARL’s) scientific goals. This will help ARL build a more rigorous state-of-the-art portfolio. The PSQs could be grouped or reshaped into fewer, more coherent themes and topic area that are less project specific and more persistent and foundational.

Taken individually, the projects within the computational methods for modeling and learning core competency appear to have a variance of quality, with some being state of the art, and others being duplicative or trailing. Chapter 4 identifies where the research may be duplicative or can be improved. Many of the projects seem to be employing good research methodologies, although the chapter provides discussion on where some improvements can be made.

Duplicative research and/or a lack of researcher’s knowledge about what others in the field are doing highlights an issue caused by the lack of mid-career scientists in the core competency. Currently, the composition of the intramural staff is largely junior with overseeing managers. Junior scientists may not be aware of the various strands of research in the field, and in the commercial space and managers may not have the bandwidth to stay current with the literature. Bringing on more mid-career level researchers can provide mentorship to the junior scientists and strong technical expertise that will help the core competency focus its scientific efforts through strong project guidance. The staff supporting the core competency were found to be competent, capable, and committed, and with this additional support, they can develop a strong core competency. ARL could consider hiring more staff whose

research training and expertise is in computational mathematics, as there is a dearth of this expertise in the core competency. These experts can also help ARL develop a coherent state-of-the art portfolio with computational mathematics at its center. They could also initiate compressed short courses for other competencies to raise awareness of ML/artificial intelligence (AI) and computational tools and techniques. In addition, a critical mass of staff could support other competencies in developing computational techniques and generative AI (GenAI) models specific to the needs of the competency. This could be very helpful to the biological and biotechnology sciences competency and the photonics, electronics, and quantum sciences competency in particular. The facilities at ARL appear to be sufficient for the needs of the computational methods for modeling and learning core competency.

Identified opportunities include the following:

• The level of rigor in the presented work for the projects “Continuous Time Digital Signal Processing Disruptive Advantages” and “Parametric Uncertainty Quantification: From Stochastic Galerkin to Deep Generative Learning, and Multiscale Modeling of Architected Materials” could be improved through more analysis to corroborate the simulations. This could help bolster confidence in the results and potentially facilitate adoption by the broader research community.
• Chapter 4 provides deep conversation of where projects in the core competency are at risk of being duplicative. See the “Research Portfolio Opportunities” section of the “Computational Methods for Modeling and Learning Core Competency” section in the chapter for this information.
• Several intramural research projects focused on such topics as adaptive video object detection, accelerated neural network inference on edge devices, and the handling of out-of-distribution data. These topics are currently being treated as distinct and separate. However, they can be viewed as interconnected through the area of federated AI techniques (e.g., federated learning and federated agents). For instance, re-training or finetuning a model via federated learning on newly collected data has been investigated as a proposed solution to handling distribution shifts in resource-constrained environments, ^b–e There are also opportunities for the core competency that exist at the intersection of computational mathematics and federated learning.^f–i
• A concerted effort to include stochastic processes, probability, and data assimilation would benefit the core competency. These areas will support current, and possibly future, ARL topics of interest in reinforcement learning, edge computing, and federated learning.
• Investment in multimodal and multi-fidelity learning capabilities will benefit ARL’s need to process information that may be compromised (in quality and/or quantity), as well as information coming from various sensing mechanisms (including audio, text, and video).^j–m
• Large language models (LLMs) are an emerging research direction with applications relevant to this core competency:
  • Researchers across all three core competencies expressed an interest in pursuing research in areas such as LLM inference at the edge.ⁿ
  • The application of LLMs for wireless communication networks would connect universal computation research in the computational methods for modeling and learning core competency with the resilient and adaptive communication networks core competency. For instance, Shao and colleagues^o recently introduced several promising wireless intelligence use cases, including power allocation, spectrum sensing, and protocol understanding.
  • There is growing interest in employing LLMs in scientific computing in areas such as material science^p and genomics.^q
• Stronger representation in computer science and a more concerted effort at ARL to participate in top-tier ML and embedded systems conferences (e.g., Computer Vision and Pattern Recognition, the International Conference on Computer Vision, the Association for Computing Machinery [ACM]/Institute of Electrical and Electronics Engineers [IEEE]

• Design Automation Conference [DAC], the IEEE/ACM Design, Automation and Test in Europe Conference, and the ACM Transactions on Embedded Computing Systems) is recommended. Such engagement would keep ARL apprised of the latest research developments relevant to its projects and also help with recruiting.

Cyber Defense and Cybersecurity Core Competency

Overall, the presented extramural project portfolio was appropriate. The work was evaluated in three focus areas: trusted autonomy, defensive deception, and adversarial clean-label defense ML. Leading researchers and institutions engaged in the research were recognized. The results are being published in notable venues. The work appears to address core competency needs and the PSQs, which are reasonable, good questions. The intramural work on trusted autonomy (Autonomous Intelligent Cyber-Defense Agents for Army Vehicle Platforms project) also appears reasonable and generally reflects a broad understanding of the underlying science and technology. Some opportunities for enhancement of the impact of this work are discussed in Chapter 4. The research into autonomous intelligent cyber-defense agents for vehicles and vehicle cyber resilience appears to be well-structured. The effort has a strong scientific lead, and the team has reasonable future research planned. The research being done at ARL appears to be on par with that of external research. The work in cyber deception was technically sound but lags behind what has been accomplished elsewhere and appears to be disjointed from what is available already commercially, and from what may be feasibly deployed in a real environment. See Chapter 4 for a broader conversation concerning this.

The composition of the intramural core competency portfolio of projects appears to be fairly narrow, and ARL may want to consider taking a broader view of the cybersecurity life cycle to include elements of design, response, and recovery in addition to detection and defense. Consideration needs to be given to the breadth of cybersecurity topics to identify potential research that may apply in an operational environment. This will require balancing available resources with overall needs, and the possible formulation of corresponding intramural PSQs. Consulting external documents such as the National Institute of Standards and Technology (NIST) NICE education model and the NIST Cybersecurity Framework may help to seed a broader view of the field.

Generally, ARL personnel are competent and dedicated to their work. In particular, scientific leaders are among the personnel involved with the extramural projects. There was a good cross-section of personnel from institutions with different profiles and foci, and they were producing results appearing in well-recognized, peer-reviewed venues. This also speaks to competency of the program managers who select and interact with those researchers. There is a distinct shortage of intramural researchers with advanced degrees in computer science coupled with a demonstrated record of core computer science research. ARL may want to consider bring in more expertise in this area.

In terms of ARL’s facilities supporting the competency, generalized computing, and virtualization appears to be available to support most projects. Missing resources included a reasonable collection of commercial and open-source products such as various honeypots and firewalls, Intrusion Detection System products, and Security Information and Event Management tools (to name a few). Along similar lines, a more robust infrastructure needs to also house additional data sets, particularly for topics such as dynamic attacker/defender interactions and incorporating human decision theory models. Having processors for deployed platforms of interest available in a laboratory setting would provide better fidelity and flexibility for the various experiments in this project domain. More generally, laboratory resources with instances of Internet of Things, unmanned aerial vehicles, and AI engines (as examples) need to be available and supported within the intramural work at ARL, both for experimental work in future projects, and to ensure that a core competency familiarity is maintained.

Identified opportunities include the following:

• ARL intramural researchers and managers could enhance connections to relevant partners in areas such as cybersecurity, information assurance, and information manipulation. Connections to research-focused groups in industry, such as Microsoft Research, Google Research (e.g., its team focused on adversarial AI), and Amazon’s AI team, would be also beneficial. Several universities have established research centers focused on these topics as well. The Computing Research Association’s (CRA’s) CRA-I committee could potentially facilitate that type of interaction. Connections could also be made to relevant federally funded research and development centers (FFRDCs). Additionally, connections with higher

• educational institutions, including those that have the Department of Homeland Security’s National Security Agency (NSA) Center of Academic Excellence-Research (CAE-R) designations, could be established. These connections could increase awareness of ARL’s extramural opportunities and may help increase interest in internships and permanent positions at ARL.
• While it is not recommended that the extramural research portfolio be entirely refocused on AI/ML, ARL’s extramural managers could ensure that it has appropriate coverage of those topics particularly in the context of information integrity research areas.
• Consideration could be given to the breadth of cybersecurity topics to identify potential research that may apply in an operational environment. Additional areas may include reconstitution, active defense, supply chain assurance, insider threat, and secure by design.
• Generally, across all competencies, ARL work might be better focused by explicitly defining measures of success, threat models, and assumptions.
• There are now many products and companies in the commercial marketplace offering deception and honeypot technologies as commercial off-the-shelf and government off-the-shelf products. Examples of these companies include Countercraft, Acalvio, Attivo, Illusive Networks, TrapX, Smokescreen, and others. The current ARL research in honeypots has been surpassed by some of these offerings and by other research efforts. ARL researchers working in this domain will need to broaden their awareness of commercial products in order to stay current and avoid duplicative efforts.
• Sandia National Laboratories is beginning a new, multiyear effort in Digital Assurance for High Consequence Computing Systems. Exploring opportunities for synergistic collaboration could enhance research in ARL’s overall portfolio in information assurance.^r
• NIST is coordinating several activities related to trustworthy AI/ML development.^s
• ARL personnel could work more toward external recognition, where possible, and help elevate peers to these distinctions. Senior researchers could strive to be senior members in the IEEE and/or distinguished members in the ACM; preferably, there could be several who are fellow rank within these organizations. ACM now has the potential to recognize contributions in classified domains.
• ARL could engage with the computing community to raise its profile and attract talent. Notably, extramural events with the ACM, as contrasted with IEEE, are more cybersecurity focused. Involvement with the nonprofit CRA^t might be helpful as well.
• Efforts to inject more computer science expertise from academia, industry, and other government laboratories via the Intergovernmental Personnel Act might prove beneficial—leading to new insights, collaborations, and possibly new hires. Other mechanisms, if allowable, such as bring in temporary federal employees or appointments as visiting scientist/engineer, could also be explored.
• Forming a technical advisory board made of a cross-section of senior researchers from academia, industry, FFRDCs, and other government laboratories could provide insights, ideas, and guidance on a frequent basis. The board members could be senior researchers who hold appropriate clearances.

Resilient and Adaptive Communication Networks Core Competency

The research efforts within the resilient and adaptive communication networks core competency are addressing contemporary challenges across the breadth of the fields, with a few notable gaps; using current and forward-looking analysis techniques, with a few deficiencies; and developing and making use of good and appropriate laboratory infrastructure with a few deficiencies, which are described in Chapter 4, along with pathways to remediation.

Within the classical communication and networking efforts, the scientific quality of the research, compared to other leading peer institutions, is of average quality. Notably, the fundamental challenge of network routing has not yet been addressed to a satisfactory level, and Chapter 4 provides a broader conversation on this point. Within the quantum communication and networking efforts, the scientific quality of the research, compared to other leading institutions, is of excellent quality. The high-level directions set by the PSQs are excellent. The specific programs and methodologies, as presented, that were chosen to address the PSQs, were overall well thought out. The main limitation of the quantum communication and networking effort is the lack of sufficient onsite laboratory infrastructure to perform component device and system-level characterization, testing, and evaluation. Fast-cycling cryogenic optical systems could greatly enhance ARL capabilities. Additionally, a more rapid method for purchasing miscellaneous components and consumables is needed to operate the laboratories in the manner that leading peer organizations do. In addition, the onsite facilities and resources for classical networking are appropriate and are well matched to effort goals and staff expertise. The lack of a digital twin technologies to test new network protocols and applications riding on top of the network, however, is a conspicuous void.

The core competency demonstrates a broad understanding of classical networking and quantum networking with its excellent PSQs. Individual research projects that may need more understanding of research conducted elsewhere have been identified in Chapter 4. There are also no major risks of the core competency not meeting its objectives. The research methods and methodologies used by researchers in the core competency are sound, well thought out, and complementary. The qualifications of the teams are suitable and appropriate for the efforts. The teams, however, were comprised mainly of PhD-level researchers within about 10 years of experience, and the scope of efforts could benefit from a diversity of education and experience levels.

Identified opportunities include the following:

• The core competency portfolio would benefit from additional focus on classical network challenges of multi-hop network architectures and routing in complex topologies with heterogeneous systems and in potentially adversarial scenarios.
• The core competency’s portfolio may benefit from expanding on the current quantum networking efforts to include consideration of distributed quantum enhanced sensors and to expand the distributed and networked quantum processor efforts and collaborations.
• The core competency needs to ensure its math programming research is not de-prioritized. Optimum math programming provides very important benchmarks for fast suboptimal algorithms and, at times, math programming techniques must be used when ML models reach their limits. For example, fast changing events in a battlefield environment are unique scenarios that cannot simply be represented as uncertainties in the ML model. ML needs large amounts of data for training, and in some dynamic, topology-changing environments, as seen in battlefield environments, or sudden adversarial attack scenarios, there will not be enough time for this training-based learning. In these situations, classical non-ML methods will need to be used. Additionally, in general, there needs to be a separate assessment of the complexity of all the algorithms in theoretical and quantitative form, in addition to the experimental observations.
• AI opens up new attack surfaces, and its vulnerability assessment and prevention may need to be a priority for ARL. Commercial companies are looking into all forms of attacks, including poisoning of data, manipulation of time stamps of inputs, etc. There are several important efforts elsewhere in the government (e.g., in the Department of Defense’s Chief Digital and Artificial Intelligence Office [CDAO, formerly JAIC], NSA’s Artificial Intelligence Security Center, and its R6 directorate). ARL could reach out to these entities to get updates of the latest understanding on the vulnerabilities and gaps that current mitigation techniques have not been able to address.
• Conspicuously absent from the presented portfolio is the problem of mobile network routing, which has been known to have serious scaling challenges (beyond around a dozen nodes) and throughput problems for decades. The Layer 3 routing problem will need solutions, and ARL could consider giving it priority if it is not already doing so.

• The Distributed Machine Learning at the Edge project effort, the Adaptive, Networked Analytics in Resource Constrained Environments project effort, and the Routing Strategies for Tightly Integrated Heterogeneous Networks project efforts could be further improved by considering more recent and advanced distributed and federated learning paradigms, since the current federated learning architecture as presented was fairly standard. Real-time retraining of ML models deployed in the field is vital as situations change and new sensor data present themselves.
• A promising new research area in distributed and federated ML at the edge is to investigate communication-, data-, and parameter-efficient pre-training and fine-tuning pipelines for large foundation models in edge network settings. The success of LLMs and GenAI and the dominance of big tech companies in this domain have led to a compelling need to “democratize AI.” One approach in democratizing AI is to enable the pretraining and finetuning of LLMs that support GenAI using a vast amount of edge devices with massive parallelisms. Realizing this goal is highly non-trivial and calls for fundamental breakthroughs in computing networking. These new techniques also come with new attack surfaces. Thus, in parallel the security aspect of AI would need to be addressed. One potential way to achieve this democratization of AI could be through a mixture of expert models and federated agents. See, for example, the work of Azalia Mirhoseini at Stanford University (her joint work with DeepMind).^u
• The security of ingested data in the field and updating neural nets is extremely important. An important frontier is determining how to forget old and irrelevant data. In a benign mobile situation, new information due to topology and traffic changes must be quickly incorporated, and old information must be decommissioned. Also, in an adversarial situation, poisoned data needs to be purged. Because all ingested data are used to train the weight parameters in specific instantiations, if sensed data are obsolete or bad, research must be done on how to negate their effects on weights. The fastest convergence in a dynamic environment is needed since situations can change quickly but the algorithms (e.g., neural nets with small number of nodes and trained by a small set of data) may become unstable and vulnerable with the slightest attacks. Classical statistical technique needs to be used to augment slow learning algorithms to sense the onset of attacks and react quickly. Such hybrid algorithms are not well researched or tested in realistic simulators or real-life experiments. In this vein, ARL could incorporate a security aspect to its research, and doing so may affect the approaches suggested by the panel in Chapter 4 going forward.
• Expertise in Layer 3 and higher is needed at ARL, and so, if ARL is not bringing on new collaboration projects that incorporate Layer 3 expertise, it may want to consider doing so.
• A very promising quantum networking direction is to extend the focus to include how to attach quantum sensors and processors to the end nodes of quantum networks. Strategically, this could leverage and enhance ARL’s leading role in the Washington Metropolitan Quantum Network Research Consortium (DC-QNet). It could also leverage existing analysis and hardware capabilities to determine optimum architectures for both scaling and connecting logical qubit processors, in line with IonQ efforts and collaborations. It could also determine how to develop components for distributed sensor networks, in line with leading analysis work coming from researchers such as Quntao Zhuang at the University of Southern California.^v
• Higher layer quantum network protocols (L3 and L4 the Transport Layer) are new horizons that would be important to have a place in the quantum portfolio.

__________________

^a Passages of these paragraphs taken from B. Rivera, 2024, “Network, Cyber & Computational Sciences Competency Overview,” DEVCOM ARL presentation to the committee, August 6.

^b F. Wu, C. Dong, Y. Qu, H. Sun, L. Zhang, and Q. Wu, 2022, “CIOFL: Collaborative Inference-Based Online Federated Learning for UAV Object Detection,” IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), https://doi.org/10.1109/MASS56207.2022.00043.

^c C. Zhang, X. Liu, A. Yao, J. Bai, C. Dong, S. Pal, and F. Jiang, 2024, “Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics,” Drones 8:312.

^d S. Gupta, K. Ahuja, M. Havaei, N. Chatterjee, and Y. Bengio, 2022, “FL Games: A Federated Learning Framework for Distribution Shifts,” arXiv, https://doi.org/10.48550/arXiv.2211.00184.

^e M. Du, M. Zhang, Y. Pu, K. Xu, S. Ji, and Q. Yin, 2024, “The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness,” arXiv, https://doi.org/10.48550/arXiv.2401.14027.

^f L.-Y. Wei, Z. Yu, and D.-X. Zhou, 2023, “Federated Learning for Minimizing Nonsmooth Convex Loss Functions,” Mathematical Foundations of Computing 6:753–770.

^g H. Zhao, K. Burlachenko, Z. Li, and P. Richtárik, 2024, “Faster Rates for Compressed Federated Learning with Client-Variance Reduction,” SIAM Journal on Mathematics of Data Science 6(1), https://doi.org/10.1137/23M1553820.

^h J. Ding, E. Tramel, A.K. Sahu, S. Wu, S. Avestimehr, and T. Zhang, 2022, “Federated Learning Challenges and Opportunities: An Outlook,” CASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), https://doi.org/10.1109/ICASSP43922.2022.9746925.

ⁱ P. Yang, H. Zhang, F. Gao, Y. Xu, and Z. Jin, 2023, “Multi-Player Evolutionary Game of Federated Learning Incentive Mechanism Based on System Dynamics,” Neurocomputing 557:126739.

^j Y. Qu, J. Nathaniel, S. Li, and P. Gentine, 2024, “Deep Generative Data Assimilation in Multimodal Setting,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

^k F. Bao, Z. Zhang, and G. Zhang, 2024, “A Score-Based Filter for Nonlinear Data Assimilation,” Journal of Computational Physics 514:113207.

^l E. Walker, N. Trask, C. Martinez, K. Lee, J.A. Actor, S. Saha, T. Shilt, D. Vizoso, R. Dingreville, and B.L. Boyce, 2024, “Unsupervised Physics-Informed Disentanglement of Multimodal Data,” Foundations of Data Science 7(1):418–445, https://doi.org/10.3934/fods.2024019.

^m F. Stadtmann, E.R. Furevik, A. Rasheed, and T. Kvamsdal, 2024, “Physics-Guided Federated Learning as an Enabler for Digital Twins,” Expert Systems with Applications 258:125169.

ⁿ Z. Yu, Z. Wang, Y. Li, H. You, R. Gao, X. Zhou, S.R. Bommu, Y. Zhao, and Y.C. Lin, 2024, “EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting,” arXiv, https://doi.org/10.48550/arXiv.2406.15758.

^o J. Shao, J. Tong, Q. Wu, W. Gao, Z. Li, Z. Lin, and J. Zhang, 2024, “WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence,” arXiv, https://doi.org/10.48550/arXiv.2405.17053.

^p Y. Hu and M.J. Buehler, 2023, “Deep Language Models for Interpretative and Predictive Materials Science,” APL Machine Learning 1:010901.

^q M. Zvyagin, A. Brace, K. Hippe, Y. Deng, B. Zhang, C.O. Bohorquez, A. Clyde, et al., 2022, “GenSLMs: Genome-Scale Language Models Reveal SARS-CoV-2 Evolutionary Dynamics,” bioRxiv, https://doi.org/10.1101/2022.10.10.511571.

^r Sandia National Laboratories, “Digital Assurance for High Consequence Systems,” https://www.sandia.gov/research/digital-assurance-for-high-consequence-systems, accessed October 21, 2024.

^s National Institute of Standards and Technology, “Trustworthy and Responsible AI,” https://www.nist.gov/trustworthy-and-responsible-ai, accessed December 20, 2024.

^t Computing Research Association, “Computing Research Association,” https//cra.org, accessed October 16, 2024.

^u B. Brown, J. Juravsky, R. Ehrlich, R. Clark, Q.V. Le, C. Ré, and A. Mirhoseini, 2024, “Large Language Monkeys: Scaling Inference Compute with Repeated Sampling,” arXiv, https://arxiv.org/abs/2407.21787.

^v Z. Zhang and Q. Zhuang, 2021, “Distributed Quantum Sensing,” Quantum Science and Technology 6:043001.

BOX 1-3
Photonics, Electronics, and Quantum Sciences Competency

The focus of the photonics, electronics, and quantum sciences competency is on strategically determining scientific investments that ensure future technology provides a full spectrum of information and decision dominance across all domains by driving new discoveries in photonics, electronics, quantum science, and sensing. Within the competency are four core competencies: electronics, photonics, sensing, and quantum science. The photonics core competency focuses on low-signature, secure comms, precise timing tools, and multifunctional, high-performance information and sensing systems. Its research focus areas include integrated photonics, radio frequency (RF) photonics, meta-optics and optical frequency and timing. The electronics core competency focuses on novel functional materials and devices for ultra-efficient, novel electronics architectures and approaches to advance sensing, communications, and processing at the tactical edge. Its research focus areas include topological electronics, ferroelectric field-effect transistors (FeFET) and material-design-HW/SW ecosystem and neuromorphic computing. The quantum science core competency focuses on quantum sensing, timing, computing, and networking that exceeds classical limitations. Its research focus areas include neutral atoms, ions, solid-state defects, and micro/macro resonators. The sensing core competency focuses on expanding and fusing sensing modalities, while exploring new concepts in electronic and mechanical sensing. Its research focus areas include deep sensing, position and inertial sensing, multi-modal sensing, decision algorithms, autonomous sensing, and sensor integration.^a

Electronics Core Competency

Overall, the electronics core competency’s research efforts are quite solid—they are significant, broad, and competitive with the research efforts in related laboratories. The priority scientific questions (PSQs) guiding the electronics core competency are state of the art. All the efforts from two-dimensional materials, device structures, circuits, and neurally inspired system development are producing cutting-edge research results. The materials and device-related research have a great mixture of intramural and extramural research. The strong device-to-circuit-to-system effort has an excellent intramural effort. Additionally, the teams supporting the core competency are using appropriate research methodologies typical of the best integrated circuit (IC) design communities in academia and in other research groups (and commercially). The approach for IC design, IC verification, and IC testing are all consistent with the best practices from the top groups in the field. The core competency also appears to have a good understanding of research conducted elsewhere. In the area of IC design, the team has been growing its research capacities based on knowledge of the field at large, and on the device side, it appears to have a solid understanding of the research in the field. The Army Research Laboratory (ARL) has a strong set of capable intramural and extramural scientists. ARL may consider leveraging its extramural research partners to help accelerate some of the intramural device level research. ARL could also connect with more IC analog design experts through more extramural collaborations. Such connections could be helpful to help build those capabilities and bring them in-house at ARL. Finally, the facilities and resources supporting the electronics core competency were sufficient, and there are no concerns in this regard.

Identified opportunities include the following:

• The device-to-circuit to system group could add extramural efforts that could complement the intramural research program. There is a significant amount of fundamental research along these lines that could assist intramural efforts and be complimentary.
• ARL could build up its analog, mixed-signal, digital IC design, and related capabilities, which are essential for developing many applications. A lack of these capabilities can limit what can be done with materials and device-level research and many system designs.
• ARL could connect to 6.1 level circuit and systems efforts being done by universities. This would include a focus on digital design, low-power analog design, and mixed-signal design; analog/mixed-signal synthesis tools; and configurable analog capabilities. Experts who may supply guidance include David Graham at West Virginia University, Siddharth Joshi at Notre Dame, Tinoosh Mohsenin at Johns Hopkins University, and Shantanu

• Chakrabarty at Washington University in St. Louis.
• For IC design, one important component to consider is on-chip detection and learning typical of machine learning accelerators
• ARL could also consider building up research efforts and collaborations on advanced packaging and three-dimensional heterogeneous integration, which are emerging areas.
• Once ARL builds some of its capabilities, for its overall electronics research, ARL could also consider academic linkages to the University of Texas at Austin’s Texas Institute for Electronics and the Defense Advanced Research Projects Agency’s Next-Generation Microelectronics Manufacturing effort.
• External researchers in academia and industry could help accelerate some of the devices to circuit-level research. For example, consulting a circuit designer with expertise in building polarization imagers (e.g., individuals like Viktor Gruev at University of Illinois Urbana-Champaign) could complement the ARL polarization imager effort.

Photonics Core Competency

The overall scientific quality of the photonics core competency is very good, and the core competency has high-quality scientists and engineers. The intramural work is on par, both in size and scope, with research in the field being done at many large academic institutions. The extramural research portfolio is focused on selected forefront research areas and includes work at institutions (University of Southern California, New York University, Australian National University) that can be considered “research peers” with the ARL photonics research staff. The overall scientific quality of the research being produced by the 18 extramural collaborations is very good, particularly in metamaterials, and includes work by some of the brightest young stars in that subfield (e.g., the 2018 winner of the Adolph Lomb Medal).

Photonics is a vast field, and ARL has built an impressive, program by focusing on a few key areas and recruiting and maintaining top-notch scientists and engineers to work in those areas. As a result, there is no doubt that ARL photonics researchers can compete and collaborate at a very high level with world leaders in the design and fabrication of optical chips, optical clock development, microwave photonics, and the development and use of optical metamaterials. The facilities supporting the competency appear to be very good and, in some cases, excellent.

Identified opportunities include the following:

• The compound semiconductor on insulator (CSOI) approach being pioneered by the Quantum Photonics Laboratory at the University of California, Santa Barbara, can enable the fabrication of devices that seamlessly integrate active optoelectronic components with traditional photonic integrated circuit (PIC) structures on a single chip. By strengthening and expanding ARL’s already-established contact with this group.^b CSOI technology could be brought in-house, giving it a powerful new tool for advancing the PIC program.
• ARL’s interest in RF photonics and quantum technology could be enhanced by expanding it to include a study of the entanglement of photons with radically different frequencies (i.e., microwave and optical). Work on this subject at the Institute of Science and Technology in Vienna could ultimately impact many aspects of quantum engineering, including communications, sensing, and computing.^c
• ARL could consider initiating a program in high harmonic generation by ultrafast lasers.^d This could put ARL at the forefront of atomic/molecular/optical physics research and bring in an extremely useful tool for non-destructive, three-dimensional characterization of photonic and electronic chip structures and material composition.^e
• So far, ARL’s time and frequency work has mainly focused on developing microresonators and microcombs without integrating them with atoms to build frequency standards. The group could benefit from merging its work with atomic spectroscopy to build clocks and test the feasibility of the microresonators for stable and accurate timekeeping.

Quantum Science Core Competency

The quantum science core competency is funding world-class researchers and producing world-leading results. The work is of high caliber, and the impact of several of its efforts are likely to produce high-impact results of great value to the community. The extramural program incorporating studies of clocks, strong correlations in quantum systems, and quantum materials is coherent and well thought out through with a mix of theory and experiment. It is at the very forefront of quantum science and producing exciting results that are generating much interest within the community and keeping the United States at the forefront of these fields. The PSQs presented in each portfolio were well-constructed and judiciously address the most pressing research challenges facing the community. The thoughtfulness of the PSQs speaks to the team’s understanding of the research community and is a testament to its knowledge of the work going on across the world. The questions address some of the most pressing challenges the research community is facing. The intramural quantum science program is also of the highest quality. The PSQs motivating the intramural program showcase the team’s understanding of the depth and breadth of the research community and highlight some of the most pressing issues facing the community regarding quantum sensors. ARL’s intramural research in Rydberg sensing of RF microwave radiation is a world-leading effort. The quantum science core competency portfolio is well balanced. The team has invested in several cutting-edge research areas and, through its scientific depth, breadth, and close connections with the extramural managers/collaborators, have made sound decisions for pursuing novel research areas.

The quantum science core competency as a whole demonstrated a clear understanding of the underlying science through its presentations, laboratory tours, and related conversations. This is further evidenced by the good mix of complementary theory and experiment across the portfolio. The team has a clear understanding of the research being conducted elsewhere through its connections to extramural leading world-class research groups. The offsite ARL facilities also allow the team to attract and retain top talent. The inter-relationships between the extramural and intramural efforts are exemplary. It is important to note that the team is also well-connected across the government and is participating in several White House Office of Science and Technology Policy interagency quantum working groups. The core competency research portfolio also displayed sound research methods and methodologies. Many of the talks and posters provided the reason behind the chosen technology and included the underlying theory, modeling, and simulations required to analyze the anticipated experimental data.

The intramural and extramural research teams supporting the quantum science core competency are quite excellent. ARL has built a world-class intramural and extramural research team. The research facilities are of high quality. The laboratories are well equipped with up-to-date equipment and facilities. The in-house ARL laboratories are complemented and strengthened by the offsite remote facilities (Massachusetts Institute of Technology, University of Texas at Austin, etc.).

Identified opportunities include the following:

• ARL’s Open Campus allowed ARL to bring in leading international researchers and provided a venue to bring the best in the world to bear on the most challenging research efforts. The closure of this program is currently a missing component in the main facility.
• There could be a closer collaboration between the photonics group and the quantum science groups. A metasurfaces researcher could be leveraged to facilitate ARL becoming a world leader in the interconnection of photonics and atomic and solid-state quantum systems.
• ARL may consider enhancing its collaborations between the classical sensor and quantum sensor teams within the photonics, electronics, and quantum sciences competency to facilitate greater cross-pollination. There may be some early opportunities for the quantum team to field some of its more advanced sensors or at least learn the largest impediments to operating in a field environment.
• ARL can ensure smaller research efforts are adequately resourced. ARL has spent significant resources to develop a silicon carbide foundry capability, but only a handful of researchers were involved. Additionally, the trapped ion quantum networking effort is

• small.
• ARL can ensure that it maintains an honest broker position within the community by continuing research efforts and allowing its government team to interact with the community through presentations, panel sessions, etc., to provide honest assessments.
• A more widespread dissemination of the exciting research under way at ARL would help in recruiting top talent. ARL could revisit its public affairs policy around press releases and public statements.
• The extramural program is largely focused on leading-edge work aimed at improving the performance of infrared/visible sensors. This is an extremely diverse field, and the decision to concentrate on a few promising material systems and device concepts is a good one. The academic groups selected for this work are reasonably well respected, although ARL may consider connecting with researchers at the University of Arizona whose work appears to complement current extramural efforts.
• Given the competency’s ongoing activity in opto-acoustics and quantum phenomena, it might be helpful for ARL to consider connecting with the Kippenberg group at EPFL (Swiss Federal Technology Institute in Lausanne).^f

Sensing Core Competency

Many projects within the sensing core competency evinced high-quality, innovative work grounded by strong research approaches or methodologies. The state-of-the-art research is at par (or exceeds) the work going on elsewhere. Chapter 5 provides specific commentary on the individual strengths of some of the projects. The ARL scientists supporting the sensing core competency were very impressive and showed interest and passion toward their research. The core competency could use a few additional people that have expertise in artificial intelligence (AI)/machine learning (ML), information fusion, collaboration in multi-agent systems, and human factors. Some of this expertise is available in other competencies (e.g., the humans in complex system competency, military information systems competency, and the network, cyber, and computational sciences competency), and closer collaboration with them is suggested. These competencies may also assist with sensor integration, data training, and developing algorithms and training models for deep sensing. Additionally, while there was evidence of some extramural efforts in the sensing area, more extramural engagements could be undertaken. There is much to be gained by developing a broader understanding of extramural research and enhancing external engagements and in the areas of multi-modal information fusion and AI/ML applications to sensing and fusion. ARL could also look at the latest methods for processing data generated by acoustic, seismic modalities and better incorporate ML. An example of an individual who may be helpful is Florian Meyer as well as researchers at the University of Connecticut, which include Peter Willet and Yaakov Bar-Shalom.

During the assessment, two laboratories/facilities were toured for the sensing core competency: the sensor fabrication facility, including a clean room; and the sensor hardware design and testing facility, which included an anechoic chamber. Both are well-equipped and are state-of-the-art facilities. If adequate technician support is not available, then adding technicians will facilitate rapid progress.

Identified opportunities include the following:

• As additional sensing modalities are added, it becomes imperative that optimal/suboptimal fusion is carried out for tasks such as detection, localization, and tracking. Once detections and tracks are available, some inferences on the overall situational awareness for the region of interest need to be made. This would include intent, war gaming strategies, resource management, etc. This latter part is likely to require collaboration with other ARL competencies.
• If the core competency is interested in human detection or casualty/mortality status, it could look at the work of Zeev Zalevsky at Bar-Elan University.^g

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^a Passages of this introduction come from P. Baker and P. Pellegrino, 2024, “Photonics, Electronics, and Quantum Sciences (PEQS) Competency,” DEVCOM ARL presentation to the committee, July 23.

^b J. Sun, 2024, “Advanced Nanoscale Fabrication Technologies for Meta-Optics and Integrated Photonic Devices,” DEVCOM ARL presentation to the committee, July 23.

^c R. Sahu, L. Qui, W. Hease, G. Arnold, Y. Minoguchi, P. Rabl, and J.M. Fink, 2023, “Entangling Microwaves with Light,” Science 380:718–721.

^d J.G. Eden, 2004, “High-Order Harmonic Generation and Other Intense Optical Field-Matter Interactions: Review of Recent Experimental and Theoretical Advances,” Progress in Quantum Electonics 28:197–246.

^e M. Tanksalvala, C.L. Porter, Y. Esashi, B. Wang, N.W. Jenkins, Z. Zhang, G.P. Miley, et al., 2021, “Nondestructive, High-Resolution, Chemically Specific 3D Nanostructure Characterization Using Phase-Sensitive EUV Imaging Reflectometry,” Science Advances 7(5), https://doi.org/10.1126/sciadv.abd9667.

^f M. Chegnizadeh, M. Scigliuzzo, A. Youssefi, S. Kono, E. Guzovskii, and T.J. Kippenberg, 2024, “Quantum Collective Motion of Macroscopic Mechanical Oscillators,” Science 386(6725):1383.

^g N. Ozana, I Margalith, Y. Beiderman, M. Kunin, G.A. Campino, and R. Gerasi, 2015, “Demonstration of a Remote Optical Measurement Configuration That Correlates With Breathing, Heart Rate, Pulse Pressure, Blood Coagulation, and Blood Oxygenation,” Proceedings of the IEEE 103:248–262.

BOX 1-4
Sciences of Extreme Materials Competency

The mission of the sciences of extreme materials competency is to serve as the Army’s lead for basic and applied research programs, technology assessments, and systems support for advanced materials, materials systems, and manufacturing science and provide the soldier with novel, unique, and affordable capabilities through materials and manufacturing science to enable creation of future transformational capabilities. The competency has three core competencies, which include invincible materials, invisible materials, and super materials. The focus of the invincible materials core competency is to mature and demonstrate advanced lightweight materials, agile manufacturing processes, modeling and simulation, and design optimization methodologies to provide survivability and durability performance improvements at acceptable weights. The focus of the invisible materials core competency is to develop materials to support the Army in offsetting vulnerabilities through materials research for camouflage, concealment, decoy, and deception (C2D2). It also focuses on reducing detectability, recognition, and targeting via materials innovations and develops and integrates materials for deception and masking into all facets of Army operations to degrade enemy decision-making and effectiveness. The super materials core competency focuses on foundational materials and manufacturing research on ultra-high-temperature materials, structural materials for munitions and missiles, gun barrel materials for all calibers, and non-energetic propellant binders.^a

Invincible Materials Core Competency

The quality of the research in the invincible materials core competency portfolio is on par with other leading national and international research institutions and funding agencies. The research for polymers, including two-dimensional (2D) polymers, is best in class. Additionally, work on armor and composites and their integration into systems for soldier and vehicle protection is best in class and remains distinct across the Department of Defense (DoD). The capabilities and subject-matter expertise in powder processing of metals and ceramics (e.g., cold spray) and the ability to produce complex shapes is also unique to the Army Research Laboratory (ARL) and across DoD. The teams of intramural and extramural scientists and engineers supporting the core competency portfolio contain subject-matter experts that are well qualified to perform the work, and, in particular, the portfolio of scientific expertise of extramural scientists is impressive and diverse. The list of external investigators is extensive and includes many well-established senior academics from leading R1 universities.^b

The ARL extramural projects portfolio of projects is impressive and covers a wide spectrum of research, including polymer chemistry, materials science, mechanics, and computational science. A strong emphasis on modeling and computational approaches is a signature of this portfolio, and research methods and their employment in the presented projects are sound. This portfolio demonstrated a broad understanding of the underlying materials science and mechanics being conducted in the field at large.

The facilities supporting the ongoing intramural and extramural research are top notch, especially as they relate to polymer processing. Novel capabilities exist to support armor and munitions synthesis and processing and testing. For example, powder processing capabilities continue to be best in class, including new cold spray investments. The staff expertise matches well to these facilities and resources, which ensures proper utilization of these facilities.

Identified opportunities include the following:

• While the extramural portfolio is very broad (polymer chemistry, particulate materials, mechanics, etc.), the majority of the highlighted projects focused on quasistatic experiments. Although this is important, as ARL choses new collaborations, a focus on dynamic behavior of relevant materials systems will need to be maintained.
• Opportunities exist to strengthen physics-based modeling and simulation, especially microstructure-driven approaches, to enhance ongoing artificial intelligence (AI) and machine learning (ML) efforts. Verification and validation and uncertainty quantification needs to be prioritized and may help to inform and streamline research efforts on future projects and the following current projects: Machine Intuitive Design of Army Steels, Next Generation Thermoplastic Composites, and Dynamic Response of Hybrid Composites.

• Significant efforts are being sponsored by the Air Force Research Laboratory (AFRL), the Office of Naval Research (ONR), and the National Nuclear Security Administration (NNSA) in high-throughput materials discovery, synthesis and processing, and testing (e.g., high-strain-rate testing) of metals and alloys for extreme environments. Connecting to these efforts may afford new opportunities and avoid duplicative efforts.
• The core competency can enhance its connection to resin and polymer industries (Dow, DuPont [especially DuPont Adhesives and Sealants], Olin, Huntsman, BASF), which will benefit future and current projects, including Resins with Adaptive and Reversible Properties, 2D Polymers: High Performance Films for Soft Armor and Optical Control, and Polymer Networks.
• Increased attendance at national and international conferences is suggested. Relevant conferences include the following: The Minerals, Metals & Materials Society (TMS) Annual Meeting and Exhibition, the Materials Science and Technology’s Technical Meeting and Exhibition, American Physical Society meetings, Materials Research Society (MRS) meetings, American Society for Composites Annual Technical Conferences, and specialty conferences.
• Given the strong computational mechanics emphasis of this portfolio, there is an opportunity to build connections with other ARL research thrusts/directions. Materials processing under extreme mechanical, thermal, and electromagnetic conditions could help investigate the effects of extreme fields and their interactions on materials response, damage, and failure.
• If the core competency researchers are not already doing so, they can connect with the biosynthesis and biomaterials core competency researchers to determine potential collaborative efforts on polymer research.
• The core competency staff may consider expanding their efforts to study materials in extremes to further include thermal and electromagnetic environments and coupled environmental responses.
• ARL could consider diversifying its extramural collaborations to grow its university network and expand its potential hiring pipeline. This may be accomplished by leveraging other AFRL, ONR, DOE/NNSA, and/or National Science Foundation–supported efforts and centers.
• ARL could consider encouraging more publications in peer-reviewed journal articles, to grow their scientists’ reputations. Possible peer-reviewed journals to consider may include Metallurgical and Materials Transactions A, Materials Science and Engineering A, Materials & Design, Scripta Materialia, Acta Materialia, Science, Nature Materials, Nature, Composites Science and Technology, and Composites Part A: Applied Science and Manufacturing.

Invisible Materials Core Competency

The extramural efforts within the invisible materials core competency are robust and productive. They have yielded multiple presentations and peer-reviewed publications in high-quality respected journals such as Science, Nature Communications, and Advanced Materials, with a few notable results that include self-healing polymer films, cephalopod-inspired dynamic structured color, and adaptive self-assembled materials. These efforts, that are performed by leading faculty at major academic institutions, represent the state of the art. Here, the extramural portfolio leads the way in scientific research and innovation, on par with other major DoD and non-DoD funding agencies. The intramural researchers have also achieved a great deal of success with their projects, which are discussed in Chapter 6.

Both intramural and extramural researchers within the core competency demonstrated that they have a broad understanding of underlying sciences and research conducted elsewhere. A deep understanding of the underlying science, both theoretically and experimentally, was clearly

demonstrated. Both extramural and intramural researchers are using sound research methods and methodologies. The interaction with the ARL researchers during the review demonstrated the researchers’ grasp of the methodologies at their disposal, and well-thought-out reasoning for selecting the methods for particular projects.

The assessment criteria asked for commentary on the overall balance of the competency’s research portfolio (e.g., core competencies, partnerships, supporting extramural partners, etc.) to address the cumulative competency goals. The projects described represent a good balance of extramural and intramural research, and there are no concerns in this regard. Additionally, there are no risks identified for the core competency not meeting its objectives. The invisible materials core competency team is highly qualified to perform state-of-the-art research. The efforts of ARL researchers in presenting and disseminating their results at high-profile conferences, such as the American Chemical Society, the International Society for Optics and Photonics (SPIE), and MRS are laudable. ARL also has unique facilities including experimental capabilities that can be of interest to researchers from outside the ARL.

Identified opportunities include the following:

• Several ARL researchers are working on engineered electromagnetic materials and, separately, on engineering thermal and phononic flows within materials. Closer communication between these groups may open up new scientific avenues, since the equations describing transport of elastic and electromagnetic waves are similar to each other.
• Regular presentations or attendance by ARL researchers at national or international conferences is important. Relevant conferences include MRS, Conference on Lasers and Electro-Optics (CLEO) conferences for visible/infrared materials, and a large number of topical meetings that are often co-organized by Optica, SPIE, and the Institute of Electrical and Electronics Engineers.
• Should access to ARLs unique facilities be available to wider research and technology communities, it would open new areas for collaboration between ARL and wider community and will likely instigate further research.

Super Materials Core Competency

The super materials core competency research is on par with other leading research institutions, both nationally and internationally. All the researchers are using state-of-the-art methodologies, fabrication facilities, and analysis tools. The experimentalists and the modeling and simulation personnel within the core competency are interacting well—exchanging insights and overviews. The ARL scientists are excellent. It is evident that clear leadership is being provided to this core competency, both intra- and extramurally, and the managers are well-attuned and sensitive to the scientific goals of the core competency. Still, ARL could more strongly establish its knowledge of the state of the art in its presentations and written documents (perhaps through a slide devoted to its knowledge of state-of-the-art research). This could help to demonstrate ARL’s understanding of research done elsewhere, because an understanding of outside research was not always apparent.

The addition of researchers working at the interface of ML and material science would be important for developing models that are grounded in the fundamentals of material science and would help in extracting scientific insights from physics or chemistry-based ML models that can aid in improving processing and/or desired properties. A dedicated polymer chemist could also be added, because current and future problems lie in this research domain. The addition of one or more ceramists, or high-temperature materials experts, would also benefit the research efforts in ceramic processing and carbon-carbon (C/C) composites.

The facilities supporting the core competency are exceptional and world class. The processing facilities included hot presses and autoclaves, as well as thermoplastic extruders. All of the equipment was of large enough scale to conduct research that goes beyond, and differs from, what is found in typical academic institutions. Metals processing also seems strong. Investing resources to help maintain the facilities is encouraged. The assessment criteria asked for comments on any specific areas, including, if found, where the research may be at major risk of not meeting its objectives, and so, it’s important to note that there are no major risks of the research not meeting its objectives.

Identified opportunities include the following:

• Most of the manufacturing work for ARL is provided by external collaborators. Knowledge of smart manufacturing processes and additive manufacturing capabilities, and limitations, could help broaden the range of material possibilities for intramural researchers. This will also help to guide researchers to assess the feasibility of materials from the fabrication perspective. The investigation and application of smart manufacturing techniques is also encouraged. ARL could also consider connecting to various programs on AI/ML (e.g., AFRL and NASA) for the additive manufacturing of metals.
• Projects focused on manufacturing and processing science are rich in data (during and after manufacturing). Exploiting more data science-based approaches could get fundamental scientific insights that connect processing-structure relationships and potentially accelerate the manufacturing process by pin-pointing the limiting process.
• Verification and validation of all models could provide confidence in the predictive capabilities of the models to guide experiments versus adjusting models and inputs to match experimental results.
• The PSQs are all good, state-of-the-art questions. Many of the PSQs, however, were written very broadly. There may be a necessity for this design, however, depending on how the PSQ will be utilized, it may be important to determine where the PSQ’s may be too broad.
• ARL could connect its research areas more precisely with the broader processing science of other academic and government laboratories. This can be accomplished through an in-depth literature understanding of the state of the art, interactions with other laboratories via visits, technology committee involvement, and conference attendance. Some individuals ARL may consider connecting with include Lavina Backman at the Naval Research Laboratory, and Lisa Rueschhoff and Vikas Varshney (who works on material simulations at the AFRL).
• Microstructure-driven approaches could be added by utilizing AI and data from other models to determine preferred microstructures to achieve desired properties for a specific material of interest (e.g., ultrahigh-temperature ceramics, C/C composites). This knowledge could be applied to guide the processing procedures that will achieve desired microstructures.
• The core competency could increase their presence and participation at Conferences. Relevant conferences include the following: the American Ceramic Society (ACerS) Annual Conference; ACerS International Conference and Expo on Advanced Ceramics and Composites; the TMS Annual Meeting and Exhibition; International Conference on Composite Materials; The Society for the Advancement of Material and Process Engineering (SAMPE) Conference and Exhibition; Gordon Research Conferences; and the DYMAT (European research association in the field of Dynamic Behavior of Materials).
• There was a sense of some siloing amongst research occurring within the intramural research efforts ARL. Being intentional with gathering technology professionals to share research successes and challenges could be beneficial. Creation of a research forum or internal seminar series is suggested with possible incentives for attendance.

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^a S. Weingarten, 2024, “Story of the Competency Structure—Sciences of Extreme Materials,” DEVCOM ARL presentation to the committee, June 4.

^b See the Carnegie Classification of Institutions of Higher Education website at https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification, accessed December 20, 2024.