Summary

The U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) is the U.S. Army’s sole fundamental research laboratory focused on cutting-edge scientific discovery and technological innovations that offer great potential to strengthen the U.S. Army. The mission of ARL is to operationalize science for transformational overmatch in support of persistent Army modernization.¹

In the fall of 2022, ARL’s scientific research efforts were reorganized into 11 competencies, which include the following: biological and biotechnology sciences; electromagnetic spectrum; energy sciences; humans in complex systems; mechanical sciences; military information sciences; network, cyber, and computational sciences; photonics, electronics, and quantum sciences; sciences of extreme materials; terminal effects; and weapons sciences. Each competency has within it varying numbers of “core competencies,” which are specially defined areas of research focus. See Appendix B for the full list and descriptions of ARL’s competencies and core competencies.

For this 2024 National Academies of Sciences, Engineering, and Medicine assessment, the Army Research Laboratory Technical Assessment Board (ARLTAB) examined at the Army’s request the following four ARL competencies: biological and biotechnology sciences; network, cyber, and computational sciences; photonics, electronics, and quantum sciences; and sciences of extreme materials. The assessment was entirely focused on answering nine assessment criteria questions (see Appendix C) and was guided by the following overarching statement of task:

The ARLTAB was assisted by four panels, the Panel on Assessment of Biological and Biotechnology Sciences; the Panel on Assessment of Network, Cyber, and Computational Sciences; the Panel on Assessment of Photonics, Electronics, and Quantum Sciences; and the Panel on Assessment of Sciences of Extreme Materials who contributed important observations and writings for the ARLTAB’s development of this assessment report after making site visits to ARL’s Aberdeen Proving Ground and Adelphi Laboratory Center in the summer of 2024. This report presents the ARLTAB’s assessment of only the projects and programs presented within these four competencies and is not intended to portray

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¹ U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL), “We Are DEVCOM Army Research Laboratory,” https://www.arl.army.mil, accessed December 20, 2022.

the entirety of the science and technology work across ARL. The complete assessment of the work presented in ARL’s 11 competencies was built over a 3-year period, with this report being the third in the series.

In 2022, ARL was also reorganized into three directorates—the Army Research Office (ARO), the Army Research Directorate (ARD), and the Research Business Directorate (RBD). RBD centralizes laboratory business operations and fosters intramural and extramural strategic decision-making. ARO, which has been operational since 1951, is composed of more than 100 engineers, scientists, and support staff, who manage ARL’s extramural research program. ARO drives cutting-edge and disruptive scientific discoveries with an eye toward enabling crucial future Army technologies and capabilities through high-risk, high-reward research opportunities.² ARD is a largely intramural research directorate that manages the laboratory’s essential research programs, which are flagship research efforts focused on delivering defined outcomes. ARD also co-manages the core competencies within the competencies, which are discussed in greater detail below.³

Because ARL, through the competency model, is trying to organize the work of ARD and ARO under one holistic umbrella, and because extramural projects can also originate from ARD, and ARO projects can support the research efforts of intramural projects, the approach to this assessment was not to tease out what was the ARD versus the ARO research. Instead, the writings make note of extramural, intramural, and integrated competency research efforts.

Chapter 1 provides the conclusions and recommendations for each competency and across competencies. It also provides four boxes (see Boxes 1-1, 1-2, 1-3, and 1-4) with short summaries that provide answers to the assessment criteria (see Appendix C) that guided the study, as well as the most actionable management-level opportunities that were identified across the four chapters (Chapters 3–6) focused on the assessment of the biological and biotechnology sciences competency; network, cyber, and computational sciences competency; photonics, electronics, and quantum sciences competency; and, sciences of extreme materials competency. Excluded from these summary boxes is commentary on individual projects, which can be found in each competency chapter. Chapter 2 provides an overview of the assessment process. A short summary of key conclusions and recommendations found for each competency an across competencies is below.

BIOLOGICAL AND BIOTECHNOLOGY SCIENCES COMPETENCY

Through strong leadership and capable staff, the biological and biotechnology sciences competency has made terrific progress over the past 6 years since the scientific focus area was added to ARL’s portfolio. Evidence was found in the competency of strong intramural and extramural expertise with an understanding of what is being done in the field at large, strong research that is at par with the work of scientific institutions nationally and internationally, state-of-the-art facilities, strong connections to external laboratories, and the employment of sound research methodologies. Chapter 3 provides suggestions for where scientific efforts can be bolstered and a more nuanced discussion of the competency’s successes and opportunities.

Key overarching conclusions and recommendations for the competency focus on developing opportunities (e.g., mini-conferences) to increase communication and collaboration between the competency’s different scientific areas (core competencies), between the competency and others at ARL, and between intramural and extramural researchers within the competency in order to enhance cross-pollination, expedite research, and inject new disciplinary perspectives into the competency’s projects. In addition, the competency can consider expanding its synthetic biology research to include the study of new fungal genera. To do so, ARL can consider spending 2–3 months (ideally 6 months) in the laboratories of fungal experts in the external community to develop expertise in working with these

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² DEVCOM ARL, “Who We Are,” https://www.arl.army.mil/who-we-are, accessed December 20, 2022.

³ Ibid.

genera. The competency could also adopt a modern data strategy through connecting to data science leaders in the field. Adopting modern data science strategies would change the competency’s efforts to collect and process data in order to make it accessible to transformer-based artificial intelligence (AI) methodologies. Leveraging AI in this way could help to identify novel and non-obvious relationships that could catalyze new areas for scientific exploration or support existing scientific efforts. ARL could also bolster its intramural bioinformatics and data science support, which was found to be overextended owing to the fast pace of growth within this competency. Data science personnel are needed to build the requisite infrastructure to support modern AI, and foundational models and additional bioinformatics support (personnel) are required to effectively use these tools

Finally, the inclusion of supramolecular polymers was also suggested to ARL as a complementary research area that may be an appropriate addition to its biosynthesis and biomaterials research efforts and/or potentially catalyze more cross-cutting collaborations with other core competencies and competencies.

NETWORK, CYBER, AND COMPUTATIONAL SCIENCES COMPETENCY

The network, cyber, and computational sciences competency was found to have capable and committed teams of intramural and extramural researchers, many of whom are working on very important scientific problems. Many of these researchers have a strong understanding of science in the field at large. Some of the competency work is at par or exceeds the work of institutions nationally and internationally. Chapter 4 highlights where research areas are particularly strong (e.g., the quantum networking efforts). The chapter also outlines where improvements can be made in particular core competency research areas and projects to create a more rigorous state-of-the-art portfolio, develop important foundational scientific capabilities, or to bring awareness to ARL of similar research efforts in the external community.

The competency is currently under-resourced in intramural expertise and laboratory tools, and the lack of these resources is affecting the competency’s ability to operate to its fullest potential. The competency can broaden its expertise by hiring or consulting with more experts trained in computational applied mathematics, computer science, and theorists who work on Layer 3 and higher routing. ARL could also, where appropriate, broaden the competency’s intramural career level diversity (e.g., mid-career) to provide more mentorship to junior staff and strategic scientific support for projects. Other ways to add more computer science expertise into the cyber defense and cybersecurity research efforts within the competency could include developing an external technical advisory board comprised of senior researchers from government, academia, and industry who could provide consistent support to the competency.

In order to develop a state-of-the-art laboratory, ARL could also consider adding significant intramural laboratory resources to support the cyber defense and cybersecurity, quantum, and classical networking research efforts competency. ARL could add commercial and open-source products such as various honeypots and firewalls, Intrusion Detection System products, and Security Information and Event Management tools to support the cyber defense and cybersecurity research efforts. Additionally, laboratory resources could include unmanned aerial vehicles, Internet of Things, AI engines, and processors for deployed platforms of interest, along with tools such as dynamic attacker/defender interactions and the incorporation of human decision theory models. To support the quantum networking research, ARL could add fast-cycling cryogenic optical systems, which will greatly enhance ARL capabilities. For the classical networking research thrust, ARL could also add digital twin technologies to test out new network protocols and applications riding on top of the network. Building a state-of-the-art laboratory will not only provide intramural researchers with the tools they need to succeed but will help in the recruiting of more experts to ARL.

ARL will also need to ensure that the resilient and adaptive communication networks research area has sufficient mathematics programming efforts. Optimum math programming provides very important benchmarks for fast suboptimal algorithms, and, at times, math programming techniques must

be used when machine learning (ML) models reach their limits. For example, fast-changing events in a battlefield environment are unique scenarios that cannot 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, 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. ARL could also increase its focus on addressing the challenges of multi-hop network architectures, routing (Layer 3) and capacity constraints, and routing in complex topologies with heterogeneous systems and in potentially adversarial scenarios. Ensuring a focus on Layer 3 routing in particular will help to lay the foundation for the success of other ARL projects.

In terms of suggestions for developing the competency’s portfolio, ARL could explore whether a broader view of the cybersecurity life cycle (e.g., elements of design, response, and recovery, detection, and defense) could align with the scientific goals of its cyber defense and cybersecurity research efforts. ARL could also consider enhancing its focus on federated AI techniques (including traditional federated learning and emerging research such as federated agents), which may serve as a unifying topic that connects problems being investigated across different projects within the competency. Finally, ARL can consider developing a more unified scientific framework for its computational methods for modeling and learning research area that incorporates broader themes and challenges in contemporary mathematics as they relate to the scientific goals of the competency. Doing so will help the competency develop a more rigorous portfolio in computational mathematics.

PHOTONICS, ELECTRONICS, AND QUANTUM SCIENCES COMPETENCY

The photonics, electronics, and quantum sciences competency was found to have excellent and innovative intramural and extramural research efforts and a strong portfolio of scientific expertise across each of its four core competencies (photonics, electronics, quantum science, and sensing). In many cases, the work is at par, or in some areas exceeds, what is found in institutions nationally and internationally. The facilities supporting the competency are good, and in some cases excellent. Chapter 5 provides suggestions for where individual scientific efforts can be strengthened and a detailed discussion of the competency’s successes and opportunities.

Key opportunities identified for the competency include strengthening the connection between the photonics and quantum science core competencies to help facilitate ARL becoming a world leader in the interconnection of photonics and atomic and solid-state quantum systems. Bringing in a metasurfaces researcher dedicated to strengthening this collaboration was suggested as one way to connect these research efforts. Additionally, increasing communication between the classical sensor and quantum sensor teams within the competency could inspire new collaborations between the teams.

Currently, ARL’s silicon carbide (SiC) foundry may be under-resourced, and ARL will want to ensure that its SiC foundry has the appropriate staffing and resources for successful utilization. ARL may also consider building up its analog, mixed-signal, digital integrated circuit design, and related capabilities, through more extramural research effort as these are essential capabilities for the development of many applications.

SCIENCES OF EXTREME MATERIALS COMPETENCY

The sciences of extreme materials competency has excellent research efforts, many of which were on par with leading institutions nationally and internationally. Its intramural and extramural scientific staff are excellent, and its facilities are exceptional and world-class. Chapter 6 provides discussion on

where individual research efforts could be strengthened, as well as actionable suggestions for some of the core competencies.

Key opportunities identified for the competency include growing connections to polymer and resin manufacturing industries to aid in knowledge capture. The intramural researchers focused on super materials could also consider pursuing more high-risk (out-of-the-box) research. Such projects would leverage the potential of the competency’s excellent staff and tools. Such topics could be determined by enhancing engagement with other government and academic laboratories (e.g., through incoming and outgoing visits) and increasing conference attendance. ARL could also increase the competency’s portfolio of scientific expertise by adding a researcher working at the interface of ML and materials science, a polymer chemist, and more ceramists and high temperature materials experts.

The competency could also grow its efforts in verification and validation uncertainty quantification and physics-based modeling/simulation (e.g., microstructure-driven) to enhance its current AI and ML efforts in invincible materials and super materials.

The cross-cutting conclusions for the four competencies assessed in this report focus on encouraging a streamlined administrative approval process to enable conference attendance; creating bilateral forums with industry, academia, and government; continuing cross-pollination efforts through communication and collaboration; and increasing awareness of emerging computational methodologies.