Download PDF Fall Bridge on the Materials Genome Initiative September 29, 2025 Volume 55 Issue 3 The Fall 2025 issue explores the Materials Genome Initiative’s progress and future outlook, showcasing advances in autonomous experimentation, sustainable polymers, next-generation batteries, and the broader role of AI in engineering. Guest Editor's Note - The Materials Genome Initiative (MGI): Status and Future Outlook Wednesday, October 1, 2025 Author: Amit Goyal Advanced materials are key to societal development and have been at the center of technological advances since the Stone Age. They are critical to national and economic security, human well-being, and impact diverse sectors including energy, communications, transportation, housing, healthcare, defense, and food packaging. However, discovering new materials with unique and improved intrinsic properties and then manufacturing commercial devices or products that use them typically requires a long, iterative, and expensive developmental cycle that can take several decades. Taking advantage of recent transformational advances in computing capabilities, theoretical modeling, artificial intelligence and machine learning (AI/ML), and data mining, the Materials Genome Initiative (MGI) was launched by the White House in 2011 to enhance US competitiveness. The goal was to exploit these advances to discover, develop, and commercialize products in a significantly shorter timeframe and at a fraction of the cost (NSTC 2011). The aspirational goals of MGI were to reduce both the discovery and development cycle and the total cost by 50% (NSTC 2011, 2014). Figure 1a shows the founding conceptual structure of the Materials Innovation Infrastructure (MII), a key component of MGI products (NASEM 2023; NSTC 2014, 2021). It combines experimental tools, digital data, and computational modeling with AI/ML to predict a material’s composition and/or how it should be processed to achieve desired physical properties for a given application. Figure 1b depicts the Materials Development Continuum (MDC), the multi-stage, linear process of discovering and developing new materials beginning with discovery and continuing through development, property optimization, systems design and integration, certification, manufacturing, and deployment. And Figure 1c illustrates the MGI paradigm, which promotes integration and iteration across all MDC stages, enabling seamless information flow and greatly accelerating deployment of new materials at reduced costs. Following the MGI Strategic Plans of 2014 (NSTC 2014) and 2021 (NSTC 2021), the National Academies’ consensus report NSF Efforts to Achieve the Nation’s Vision for the Materials Genome Initiative: Designing Materials to Revolutionize and Engineer Our Future (DMREF) was published in 2023 (NASEM 2023). It provided important recommendations for future DMREF initiatives to increase MGI-related impact. The National Science Foundation (NSF) is one of 19 federal agencies and their associated laboratories engaged at different Technology Readiness Levels (TRLs) in MGI. Others include the Department of Commerce, Department of Defense, Department of Energy, National Institutes of Health, National Aeronautics and Space Administration (NASA), Department of Health and Human Services, and US Geological Survey. This issue of The Bridge presents perspectives from national and global leaders in MGI who are developing strategic plans and policy across federal agencies, as well as from research and innovation leaders working in key areas such as autonomous experimentation, self-driving laboratories, advanced microscopy, polymers/plastics, and energy storage. These articles provide state-of-the-art reviews, summarize progress to date, and offer recommendations for future work to fully realize the goals of MGI. Lisa Friedersdorf (White House Office of Science and Technology Policy) and James Warren (National Institute of Standards and Technology) provide an overview of progress in realizing MGI’s goals and outline opportunities for accelerating innovation. They emphasize that AI/ML can generate predictive and surrogate models that may replace physics-based models and simulations. The authors also discuss automated laboratories, autonomous experimentation (AE), and “materials digital twins” to further accelerate materials innovation. Richard Vaia (Air Force Research Laboratory), Germano Iannacchione (NSF), and Anthony Rollett (Carnegie Mellon University) summarize the path for national leadership toward a data-centric materials revolution. They highlight frameworks such as TRLs and the Manufacturing Readiness Level, Adoption Readiness Level and Materials Maturation Level (MML) frameworks to address the “valleys of death” across the MDC. Vaia and colleagues underscore the importance of collaboration between agencies focused on fundamental and discovery research, mission-driven agencies, national labs, and industry. Milad Abolhasani (North Carolina State University) reviews global progress in self-driving laboratories (SDLs), which integrate all essential components of MGI by combining AI, AE, and robotics in a closed-loop manner. SDLs can design experiments, synthesize materials, characterize functional properties, and iteratively refine models without human intervention. This capability enables thousands of experiments in rapid succession, converging on optimal solutions. SDLs are thus a critical component in realizing the full potential of MGI. Figure 2 shows how an SDL operates. Given an end goal, an SDL designs and executes experiments using materials libraries, synthesizes materials, characterizes them, and iteratively refines results with AI/ML until reaching an optimal solution. Abolhasani provides a summary of recently developed SDLs across various scientific domains and functional areas and involving different synthesis or fabrication techniques. He provides strategic recommendations on how SDLs can be further developed and integrated across various functional areas to fully realize the potential of MGI. Benji Maruyama (AFRL), Ichiro Takeuchi (University of Maryland), and Jason Hattrick-Simpers (University of Toronto) focus on advances in AE and SDLs applied to materials synthesis techniques such as physical vapor deposition, chemical vapor deposition, and electrochemical deposition, processes essential for producing advanced electrical and electronic materials, including semiconductors, superconductors, ferroelectrics, multiferroics, and quantum materials. Sergei Kalinin (University of Tennessee, Knoxville, and Pacific Northwest National Laboratory), Steven Spurgeon (National Renewable Energy Laboratory, University of Colorado Boulder, and Colorado School of Mines), and Vinayak Dravid (Northwestern University) review recent advances in autonomous electron microscopy and scanning probe microscopy for AI/ML-enabled physics discovery and materials optimization. They describe the outlook for a transition from human-operated microscopy to autonomous microscopy and then multi-instrument autonomous facilities. Rigoberto Advincula (University of Tennessee, Knoxville, and Oak Ridge National Laboratory) addresses SDLs for polymers and applications such as plastic recycling. He examines polymer engineering and science, from statistics to digital twins, illustrates the concept with a flow chemistry–based SDL, and considers how SDLs can support a circular economy by enhancing plastics recycling. Arumugan Manthiram and Tianxing Lai (The University of Texas at Austin) discuss an important functional area, advanced batteries, providing an overview of the status and outlook of different types of batteries and how AI/ML and MGI approaches have affected their development. Batteries are a perfect example of an area where new, environmentally friendly, non-critical materials could be discovered and developed using the MGI paradigm. In summary, MGI is poised to make a transformative impact on how advanced materials of the future are discovered, designed, developed, and fabricated into devices and products. This transformation is driven by the development of next-generation physics-based models enabled by advances in computing; the creation of surrogate models that provide AI-driven approximations to physics-based models, resulting in materials digital twins; the development of autonomous experimentation tools and self-driving laboratories across different functional areas; the emergence of autonomous and self-driving advanced microscopy tools; and the establishment of mature, consistent data libraries and repositories. Increased research interaction and collaboration among agencies and organizations across TRLs and MMLs is needed to address the risks and “valleys of death” traditionally encountered between discovery, development and scale-up, certification, manufacturing, and deployment. Adequate federal and industry investments made in partnership with academia must continue to support the further development of the materials innovation infrastructure with a focus on national priorities to ensure US leadership in this critical area of advanced materials that affect national security, economic security, and human well-being. Acknowledgments I thank the authors of all seven articles in this issue for their contributions and their thoughtful comments on reviewing other articles in this issue. I also acknowledge the following MGI leaders for reviewing articles and providing insightful evaluations: Kristin Persson (UC Berkeley and Lawrence Berkeley National Laboratory), William Carter (DOD), Markus J. Buehler (MIT), Rampi Ramprasad (Georgia Tech), Venkat Viswanathan (University of Michigan), Scott Miller (NextFlex), Paulette Clancy (Johns Hopkins University), Emmanuel P. Giannelis (Cornell University), and Keith A. Brown (Boston University). Support from the Office of Naval Research (ONR), Grant No. N00014-21-1-2534, is gratefully acknowledged. References Canty RB, Bennett JA, Brown KA, Buonassisi T, Kalinin SV, Kitchin JR, Maruyama B, Moore RG, Schrier J, Seifrid M, Sun S, Vegge T, Abolhasani M. 2025. Science acceleration and accessibility with self-driving labs. Nature Communications 16:3856. NASEM [National Academies of Sciences, Engineering, and Medicine]. 2023. NSF Efforts to Achieve the Nation’s Vision for the Materials Genome Initiative: Designing Materials to Revolutionize and Engineer Our Future (DMREF). Washington: National Academies Press. Online at https://nap.nationalacademies.org/catalog/26723. NSTC [National Science and Technology Council]. 2011. Materials Genome Initiative for Global Competitiveness. Online at https://www.mgi.gov/sites/mgi/files/materials_genome_ initiat ive-final.pdf. NSTC. 2014. Materials Genome Initiative Strategic Plan. A Report by the Subcommittee on the Materials Genome Initiative Committee on Technology. Washington: Executive Office of the President. Online at www.mgi.gov/sites/mgi/files/mgi_strategic_plan_-_dec_2014. pdf. NSTC. 2021. Materials Genome Initiative Strategic Plan. A Report by the Subcommittee on the Materials Genome Initiative Committee on Technology. Washington: Executive Office of the President. Online at www.mgi.gov/sites/mgi/files/MGI-2021-Strategic-Plan.pdf. About the Author:Amit Goyal (NAE) is a SUNY Distinguished Professor and SUNY Empire Innovation Professor at the State University of New York (SUNY) at Buffalo. He is a member of the National Academies’ National Materials and Manufacturing Board.