NSF and USDA-NIFA National Artificial Intelligence Research Institutes (NSF 23-610) for Agriculture, Climate, and Foundational AI

NSF and USDA-NIFA National Artificial Intelligence Research Institutes (NSF 23-610) for Agriculture, Climate, and Foundational AI is sponsored by U.S. National Science Foundation (NSF) in partnership with USDA National Institute of Food and Agriculture (NIFA) and other federal and industry partners. The National AI Research Institutes program is NSF's flagship AI investment, funding large multi-institution centers at roughly $20 million each over five years.

NSF 23-610: National Artificial Intelligence Research Institutes | NSF - U.S. National Science Foundation Not currently accepting proposals This program is awaiting a new solicitation and is not currently accepting proposals.

Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements . All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects.

NSF 23-610: National Artificial Intelligence (AI) Research Institutes Accelerating Research, Transforming Society, and Growing the American Workforce To save a PDF of this solicitation, select Print to PDF in your browser's print options.

National Science Foundation Office of the Under Secretary of Defense for Research and Engineering National Institute of Standards and Technology Capital One Financial Corp. Preliminary Proposal Due Date(s) (required) (due by 5 p. m. submitter's local time): Themes listed under Group 1 (awards anticipated FY 2024) Themes listed under Group 2 (awards anticipated FY 2025) Full Proposal Deadline(s) (due by 5 p.

m. submitter's local time): Themes listed under Group 1 (awards anticipated FY 2024) Themes listed under Group 2 (awards anticipated FY 2025) Important Information And Revision Notes New themes for Institute proposals (see Program Description). A revised description of "use inspired research" in the Project Description (I.

B) improves our emphasis on the intent for research convergence, such that use-inspired research is an expected modality of an Institute's foundational AI goals. Agency and Industry partners on this solicitation have changed.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted.

The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements National Artificial Intelligence (AI) Research Institutes Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits.

Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI research and development to create new sectors of the economy and revitalize industries.

Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

This program is a multisector effort led by the National Science Foundation (NSF), in partnership with the Simons Foundation (SF), the National Institute of Standards and Technology (NIST), Department of Defense (DOD) Office of the Under Secretary of Defense for Research and Engineering (OUSD (R&E)), Capital One Financial Corporation (Capital One), and Intel Corporation (Intel).

Group 1 - Awards anticipated in FY 2024: Theme 1: AI for Astronomical Sciences Group 2 - Awards anticipated in FY 2025: Theme 2: AI for Discovery in Materials Research Theme 3: Strengthening AI For the institute themes listed in Group 1, NSF anticipates awards to start in FY 2024; and for themes listed in Group 2, NSF anticipates awards to start in FY 2025.

Each group has a specific set of due dates and review timeline pertaining only to that group. More detail is found under Due Dates and in the timeline provided in the Program Description. Cognizant Program Officer(s): Please note that the following information is current at the time of publishing.

See program website for any updates to the points of contact. AI Research Institutes Program Team, email: AIInstitutesProgram@nsf.

gov Applicable Catalog of Federal Domestic Assistance (CFDA) Number(s): --- Mathematical and Physical Sciences --- Computer and Information Science and Engineering --- Social Behavioral and Economic Sciences --- Office of International Science and Engineering --- Office of Integrative Activities (OIA) --- NSF Technology, Innovation and Partnerships Anticipated Type of Award: Estimated Number of Awards: Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

In Theme 1, NSF and the Simons Foundation expect to co-fund up to two National AI Research Institutes. The Simons Foundation intends to provide up to $20 million and NSF intends to provide up to $20 million to support up to two new awards in FY 2024 - FY 2028, subject to the availability of funds. The average total size and duration of a grant will be $4M per year for 5 years, evenly split between NSF and SF.

NSF and partners plan to make one award in theme 2 and two or more awards in Theme 3, subject to the availability of funds. Anticipated Funding Amount: Institute awards will be made for between $16,000,000 and $20,000,000 for four to five years ($4,000,000 per year on average). Proposals outside this range may be returned without review.

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds. Who May Submit Proposals: Proposals may only be submitted by the following: Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.

Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.

Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. There are no restrictions or limits. Limit on Number of Proposals per Organization: An organization may submit no more than two preliminary proposals to this solicitation as lead institution.

This limit is solicitation-wide and applies across the groups and themes. An organization may submit up to two full proposals that correspond to preliminary proposals reviewed under this solicitation.

In the event that an organization exceeds these limits, preliminary proposals will be accepted based on earliest date and time of preliminary proposal submission, i.e., the first two preliminary proposals will be accepted, and the remainder will be returned without review. A full proposal that does not correspond to a preliminary proposal reviewed in this program will be returned without review.

Limit on Number of Proposals for Senior Personnel: An individual may be designated as senior personnel (which includes but is not limited to PI or co-PI) on at most one preliminary proposal, and at most one full proposal to this solicitation. This limit is solicitation-wide and applies across the groups and themes.

In the event that an individual exceeds this limit, proposals will be accepted based on earliest date and time of submission, i.e., the first compliant preliminary or full proposal will be accepted, and the remainder will be returned without review. Proposal Preparation and Submission Instructions A. Proposal Preparation Instructions Letters of Intent: Not required Preliminary Proposals: Submission of Preliminary Proposals is required.

Please see the full text of this solicitation for further information. Full Proposals submitted via Research. gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply.

The complete text of the PAPPG is available electronically on the NSF website at: https://www. nsf. gov/publications/pub_summ.

jsp? ods_key=pappg . Full Proposals submitted via Grants.

gov: NSF Grants. gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants. gov guidelines apply (Note: The NSF Grants.

gov Application Guide is available on the Grants. gov website and on the NSF website at: https://www. nsf.

gov/publications/pub_summ. jsp? ods_key=grantsgovguide ).

Cost Sharing Requirements: Inclusion of voluntary committed cost sharing is prohibited. Indirect Cost (F&A) Limitations: Projects in Theme 1 (AI for Astronomical Sciences) will be jointly funded with the Simons Foundation.

Half of the total allowed budget (up to $10M) must be prepared by following the NSF Proposal and Award Policies and Procedures Guide (PAPPG), and the other half of the budget (up to $10M) must be prepared by following instructions from the Simons Foundation, included with the announcement of this funding opportunity at the Simons Foundation website ( https://www. simonsfoundation.

org/grant/nsf-simons-national-artificial-intelligence-ai-research-institutes-in-the-astronomical-sciences/ ). Note that the Simons Foundation has a specific indirect cost rate policy . Other Budgetary Limitations: Other budgetary limitations apply.

Please see the full text of this solicitation for further information. Preliminary Proposal Due Date(s) (required) (due by 5 p. m.

submitter's local time): Themes listed under Group 1 (awards anticipated FY 2024) Themes listed under Group 2 (awards anticipated FY 2025) Full Proposal Deadline(s) (due by 5 p. m. submitter's local time): Themes listed under Group 1 (awards anticipated FY 2024) Themes listed under Group 2 (awards anticipated FY 2025) Proposal Review Information Criteria National Science Board approved criteria.

Additional merit review criteria apply. Please see the full text of this solicitation for further information. Award Administration Information Additional award conditions apply.

Please see the full text of this solicitation for further information. Additional reporting requirements apply. Please see the full text of this solicitation for further information.

AI is advancing rapidly, enabled and significantly fueled by federally-funded research. Increasingly sophisticated and integrated approaches for AI systems appear in applications across all sectors of the economy, and new challenges emerge for advancing, applying, and governing these promising technologies.

AI holds the potential to transform lives across our Nation through increased economic prosperity, improved educational opportunities and quality of life, and enhanced security.

At the same time, the potential capabilities and complexities of AI, combined with the wealth of interactions with human users and the environment, make it critically important to further advance our understanding of AI, including aspects of transparency, security, and control.

Among federal research investments, institute-scale activities enable multidisciplinary, multi-stakeholder teams to focus on larger-scale, longer-time horizon challenges in both foundational and use-inspired AI research, and development of the future AI workforce, as well as addressing some of society's grand challenges.

National AI Research Institutes will serve as national nexus points for collaborative efforts spanning institutions of higher education, federal agencies, industry, and nonprofits/foundations in such areas. They should also accelerate the transition of AI innovations into many economic sectors, and nurture and grow the next generation of talent.

A long-term, substantive, and highly visible investment in AI research, infrastructure, and workforce development will realize the potential of, and enable the U.S. to maintain global leadership in, AI. AI enables computers and other automated systems to perform tasks that have historically required human cognition and human decision-making abilities.

Research in AI is therefore concerned with the understanding of the mechanisms underlying thought and intelligent behavior and their implementation in machines.

The full AI endeavor is inherently multidisciplinary, encompassing the research necessary to understand and develop systems that perceive, learn, reason, communicate, and act in the world; exhibit flexibility, resourcefulness, creativity, real-time responsiveness, and long-term reflection; use a variety of representation or reasoning approaches; and demonstrate competence in complex environments and social contexts.

What is sometimes referred to as “core AI research" addresses, in general, the theory and methods that give rise to these target abilities and their implementation in machines. It includes research in all matters of learning, abstraction, and inference required for intelligent behavior as well as general architectures for intelligence, integrated intelligent agents, and multiagent systems.

Machine learning, that is, methods for solving tasks by generalizing from data, has made great advances in recent years through the combination of new algorithms, increases in computing power, and the growing availability of data.

Machine learning does not, however, encompass all of core AI; which also includes research on knowledge representation, logical and probabilistic reasoning, planning, search, constraint satisfaction, and optimization. In some lines of AI research, computational models and mechanisms of intelligence draw direct inspiration from living systems.

Biologically-inspired computing draws from connectionism, behavior, and emergence in living systems to inform algorithm and system design. Computational neuroscience contributes models based on theory and analysis of computational processes in the nervous system.

Behavioral and cognitive science informs much of the motivation and design of systems seeking to implement behavior typical of human perceptual, motor, and cognitive processes and their interactions. Also relevant is human-AI interaction, which studies the interface between people and this class of software artifacts to help bring them into more productive alignment.

Perception and communication are critical capabilities associated with intelligent behavior. Where AI is concerned, the field of computer vision studies methods that enable systems to sense and reason about the visual world.

Human language technologies (also known as “natural language processing” and “natural language understanding”) research enables intelligent systems to analyze, produce, translate, and respond to human text and speech. Intelligent systems may be able to act upon the world through embodiment. Robotics is closely aligned with but not identical to embodied AI.

While an embodied AI may be a robot, not all robots exhibit embodied AI. This solicitation does not include in the scope of this definition teleoperated robots or industrial robots that simply repeat programmed patterns of motion.

As intelligent systems amplify humans’ capabilities to accomplish individual and collective goals, research is needed to assess the benefits, effects, and risks of AI-enabled computing systems; and to understand how human, technical, and contextual aspects of systems interact to shape those effects.

Relevant research areas therefore include consideration of explainable and trustworthy AI; validation of AI-enabled systems; AI safety, security, and privacy; and the role of ethics in the design and perception of increasingly sophisticated machine intelligence.

Research in AI also encompasses novel software and hardware architectures, as well as methods for carrying out AI algorithms on a variety of computing systems and platforms, including those that operate under additional constraints such as time (e.g., real-time) or energy, or those targeting specific application classes or use cases.

Developing hardware further optimized for AI and ML algorithms or hardware offers the potential for even higher levels of performance. The above definition of AI and its principal disciplines establishes the scope of this National AI Research Institutes program. I.

B. Foundational and Use-Inspired AI Research Research in foundational AI seeks to develop theory and methods that are independent of any particular domain of application. Use-inspired AI research refers to research that has use for society in mind.

Use-inspired research seeks new methods and understanding in AI by situating the research in a domain of application to simultaneously inform progress in AI and solve particular use cases. As an example, foundational research in machine learning gave rise to breakthroughs in deep neural networks motivated by performance in controlled contexts like character recognition.

Later, the further investigation of these methods in the intersection of machine learning and linguistics led to the development of recurrent neural networks in AI while also revolutionizing language modeling for speech and text processing. We use the phrase “use-inspired” rather than “applied” to emphasize that use-inspired research is an expected modality 1 of an Institute's foundational AI research.

This solicitation seeks to support work that goes beyond merely applying known techniques and adds new knowledge and understanding in both foundational AI and use-inspired domains. Ideally there is a virtuous cycle resulting from deeply integrated convergent research in which AI advances are motivated by domain challenges, and those domains benefit from new AI advances.

AI has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits. Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in ML have made it possible for AI development to create new sectors of the economy and revitalize industries.

Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements. 1 Stokes, D. E.

, “Pasteur’s Quadrant: Basic science and technological innovation. ” Washington, DC, Brookings Institute Press, 1997 Building upon the nationwide network of AI Research Institutes, this National AI Research Institutes program solicitation will fund Institutes comprised of scientists, engineers, and educators united by a common focus on advancing the research frontiers in AI.

The program seeks to build a broader nationwide network to pursue transformational advances in a range of economic sectors, and science and engineering fields. AI Research Institutes will have as their primary focus the advancement of multidisciplinary, multi-stakeholder research on larger-scale, longer-time-horizon challenges in AI research than are supported in typical research grants.

They will accelerate the development of transformational technologies by grounding that research in critical application sectors that can serve as motivation for foundational research advances and provide opportunities for the effective fielding of AI-powered innovation. II. A.

AI Research Institutes Scope The vision of the National AI Research Institutes program is broad and ambitious. It is expected that each AI Research Institute will pursue this vision in ways that are uniquely suited to its selected research focus, facilities, collaborations, and other unique circumstances.

Proposers are encouraged to convey the unique qualities of the proposed Institute, while addressing the following desiderata common to all AI Research Institutes proposed to this program: AI Research Institutes advance foundational AI research that will have broad and lasting impact, contributing new knowledge or methods toward understanding of the mechanisms underlying thought and intelligent behavior and their implementation in machines (see the definition of AI specified above).

Institutes aimed at advancing established AI lines of research should demonstrate the potential to radically advance these areas beyond the state of the art. Institutes might also address new foundational AI research priorities that arise from rapid advances in AI and the increasing ubiquity of AI-enabled technology.

Institute proposals that do not describe a clear plan to achieve ambitious advances in foundational AI research are not likely to be responsive to this solicitation. AI Research Institutes conduct use-inspired research that both informs foundational AI advances and drives innovations in related sectors of science and engineering, segments of the economy, or societal needs.

Effective use-inspired research achieves synergy among a group of researchers to enable transformative advances in AI, related sectors, and the interfaces between these areas.

This dimension of an AI Research Institute will feature clear and compelling goals to advance AI and to accelerate the fielding of AI-powered innovation; it also enhances the transfer of knowledge through the meaningful exchange of scientific and technical information with external stakeholders such as industrial partners, public policy makers, or international organizations, as well as with the broader scientific and educational community.

Through use-inspired research, Institutes have the potential to create and share new community infrastructure, including data and software, to further research, promote reproducibility, and support education.

It is critical that proposals clearly specify how the use-inspired context for Institute research reveals the opportunities for foundational AI advances and how those foundational AI advances in turn contribute to the related sectors that define the use-inspired context. AI Research Institutes actively build the next generation of talent for a diverse, well-trained workforce.

Specifically, AI Research Institutes should leverage the visionary nature of their research foci to drive new and innovative education and development tailored toward, e.g., undergraduates, graduate students, and post-doctoral researchers, as well as through community colleges and skilled technical workforce training and other opportunities as appropriate that advance knowledge and education of AI, including public understanding of AI.

This could include innovative pedagogy and instructional materials, advanced learning technologies, project-driven training, cross-disciplinary and collaborative research, industry partnerships, and new career pathways.

Institutes should offer broad, deep, and diverse experiences to build the next generation of the AI workforce, with a focus on broadening participation among the full range of groups currently under-represented in science and engineering. AI Research Institutes should maximize their unique position to grow the next generation of talent that will provide new discoveries and leadership.

AI Research Institutes are coherent multidisciplinary groups of scientists, engineers and educators appropriate for a large-scale, long-term research agenda for the advancement of AI and the fielding of AI-powered innovation in application sectors of national importance.

The multidisciplinary nature of these Institutes will catalyze foresight and adaptability beyond what is possible in single research projects; further, the individual projects that an Institute carries out should meaningfully integrate into fundamental contributions beyond the sum of the individual projects. Each Institute will be comprised of multiple organizations working together to create significant new research capabilities .

NSF and partner organizations seek to grow the network of National AI Research Institutes in lead organizations distributed throughout the country to grow new centers of AI leadership and leveraging existing centers of excellence as appropriate.

Institutes are strongly encouraged to include organizations that can directly contribute to NSF’s commitment to broadening participation by engaging a diverse, globally engaged research community, integrating research with education and building capacity, and expanding efforts to include the participation of the full spectrum of diverse talent in STEM and diverse institutions across all geographical regions.

Participants should be meaningfully integrated into a diverse Institute that is more than just the sum of the parts. Each Institute will have a lead PI with demonstrated vision, experience, and capacity to manage a complex, multi-faceted, and innovative enterprise that integrates research, education, broadening participation, and knowledge transfer.

Each Institute will also be staffed with a Managing Director or Project Manager (distinct from the lead PI) and a suitable Management Team to oversee the operations of the Institute. An External Advisory Board is required for all AI Research Institutes. (Potential Advisory Board members should not be approached or identified until the Institute is funded.)

AI Research Institutes are nexus points for collaborative efforts .

The "nexus point" function in this program is not a mere state of being, but rather an active set of priorities, programs, mechanisms, etc., whereby an AI Research Institute pursues the continuing growth of collaborations with external partners to bring together people, ideas, problems, and technical approaches for maximum impact beyond the members and the boundaries of the Institute itself.

As nexus points, Institutes have the potential to continue to connect with new partners with the best teams and approaches from institutions of higher education, federal agencies, industry, nonprofits/foundations, centers/institutes, and national networks.

As nexus points, Institutes promote organizational collaborations and linkages within and between campuses, schools, and the world beyond, and further the Institute’s mission to broaden participation in research, education, and knowledge transfer activities through a network of partners and affiliates. II. B.

Institute Themes in GROUP 1 Awards anticipated in FY 2024: Proposals are being solicited in the following high-priority areas for awards anticipated in FY 2024. Due dates listed for Group 1 apply for submissions to the themes in this group.

Theme 1: AI for Astronomical Sciences With current and future astronomical experiments poised to flood the field with petabytes of high-quality imaging and spectroscopic data over a wide range of wavelengths of light and with a high temporal cadence, AI technology will be essential for mining and analyzing these data.

The primary goal of an AI Institute in astronomy is to bring together astronomy and AI experts to tackle important challenges in astronomy, as well as the advances in AI that are needed to overcome these challenges. An AI institute will serve as a hub and resource for the broader astronomical community by making tools publicly available and by promoting the education and training of the astronomical community in AI methods.

Proposals can address any relevant combination of AI use cases. Some examples are provided below. This list is meant to stimulate thought about the many potential application areas and is not prescriptive.

Clean raw astronomical imaging, spectroscopic, or time series data by removing sources of statistical and systematic noise. Derive accurate estimates of physical parameters of objects or extract statistical measurements directly from raw observational data. Classify objects on the fly for rapid follow-up observation.

Find rare events using anomaly detection. Estimate physical model constraints directly from raw observational data. Predict the behavior of complex theoretical simulations to reduce their computational cost.

Develop fast and accurate emulators that can be used in statistical modeling of data. Create an “AI astronomer” who can assist with exploring multidimensional data sets or who knows the astronomical literature. Many of these applications may require foundational advances in AI to succeed.

For example, advances may be required in dealing with especially large and complex data sets, in adding knowledge of physical laws into AI models, or in developing interpretable AI methods with well understood error properties. Proposals should clearly justify both the selection of the targeted astronomical use cases and the breakthroughs needed in foundational AI research.

Proposals are also encouraged to discuss the potential for those AI advances to benefit AI research more broadly or to impact application fields beyond astronomy. Proposals are expected to convey a vision and approach that is appropriate for the scale of these Institutes and that produces transformative outcomes. Proposals should also describe how the Institutes will connect with the broader community to disseminate knowledge.

The proposed structure, activities, and management of the Institutes to achieve these goals should be clearly described. This theme is partially funded by the Simons Foundation.

Each institute funded under this theme will receive two separate awards of up to $10M, one in the form of a cooperative agreement at NSF as described in this solicitation, and one award from SF in accordance with SF award procedures and consistent with applicable law. See Proposal Submission Guidelines for detailed procedures on how to structure project plans and budget submissions. II.

C. Institute Themes in GROUP 2 Awards anticipated in FY 2025: Proposals are being solicited in the following high-priority areas for awards anticipated in FY 2025. Due dates listed for Group 2 apply for submissions to the themes in this group.

Theme 2: AI for Discovery in Materials Research AI has the potential to revolutionize materials discovery by integrating first principles from materials science, physics, and chemistry with heterogenous multi-dimensional experimental and synthetic data streams to scale and accelerate development.

AI can expand the types and properties of materials considered through augmentation of human intuition and by tailoring discoveries to address societal challenges, such as sustainability and those in emerging industries. A successful Materials AI Institute will transform the materials discovery landscape, enable new AI-based capabilities, and be responsive to societal challenges and industrial needs.

Advances in AI have the potential to transform materials research in several ways. Some potential lines of research are provided below. This list is meant to stimulate thought about use-inspired research in the intersection of AI and materials, and is not prescriptive.

Multi-modal data integration and dataset development: Data streams that describe material properties and behaviors based on different types of variables are ubiquitous in materials science and span different length/time scales and represent a vast set of modalities, such as simulation, synthesis experiments, and characterization experiments.

Research in AI-enabled frameworks for materials research have the potential to catalyze the generation of insights by integration of heterogeneous multi-modal data streams across different length/time scales. In addition, tools and mechanisms are needed to accelerate the development of new data sets with appropriate diversity, speed, and volume to empower ground-breaking AI methods for targeted materials science problems.

Foundational AI advances driven by materials research: Extending and tailoring AI methodologies to materials science and its unique data streams creates an opportunity to develop fundamentally new algorithmic and methodical frameworks in AI for materials discovery.

From a bottom up (i.e., data-driven) direction, foundational AI advances in this field should fully capture and incorporate the unique characteristics and interactions evident in materials science.

From a top down (i.e., knowledge-guided) perspective, the principles of materials science hold the potential to ground data-intensive operations in the rich mathematical complexity and multi-scale nature of the different physical and chemical relationships inherent to materials. The integration of both data-driven and knowledge-guided AI holds even greater potential to lead to significant advances in materials.

First synthesis to synthesis at scale: Materials synthesis at scale is a major challenge in materials discovery. The precision and level of understanding required spans various complex phenomenological challenges.

Research in the intersection of materials science and AI has the potential to sustainably synthesize materials at scale while mitigating the complex phenomenological challenges related to materials properties, materials processing for reliable synthesis, efficient characterization for measurement of relevant properties, and statistics-based understanding of various stochastic elements present in large-scale systems.

Use-inspired AI research for materials science has the potential to revolutionize materials discovery and lead to new technologies that can address complex societal challenges. Human-augmented materials design: While AI holds great potential to automate discovery, it remains critical that this discovery be guided by and responsive to materials scientists who will collaborate with AI systems.

The interfaces that mediate AI-driven materials research should be guided by principles for effective human-AI interaction and collaboration. Principled mechanisms of interaction between human experts and AI-augmented technology can change how materials designers think about design challenges and catalyze human creativity in new and unexpected ways—for example, shortening the requirements-design-synthesis-experiment cycle.

Effective guidance from domain experts will also help ensure that the design of novel materials is conducted ethically and safely. Interpretable materials AI: As AI accelerates new advances and insights in materials science, human understanding of materials will be advanced even further to the extent that the operations of the system are interpretable by materials scientists.

A system with transparent and explicable operations will have a higher potential to contribute to the discovery of new fundamental principles in materials science. For example, might successful AI materials models predict the essential ingredients of microscopic Hamiltonians for quantum materials? Can they provide clues to develop new concepts that expand theory and computation to enable humans to reach the same or better solutions?

The more interpretable the materials AI system, the greater the opportunity for materials scientists to explore new frontiers