The future of artificial intelligence and the mathematical and physical sciences (AI+MPS) <sup>*</sup>
Abstract This community paper developed out of the National Science Foundation Workshop on the Future of artificial intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community’s perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.
Authors
- Soledad Villar (ORCID: https://orcid.org/0000-0003-4968-3829)
- Andrew Ferguson (ORCID: https://orcid.org/0000-0001-9953-7542)
- Pratyush Tiwary (ORCID: https://orcid.org/0000-0002-2412-6922)
- Lars Ruthotto (ORCID: https://orcid.org/0000-0003-0803-3299)
- Yuan-Sen Ting (ORCID: https://orcid.org/0000-0001-5082-9536)
- Marisa LaFleur
- J. J. Thaler
Institutions
- Johns Hopkins University (US)
- Emory University (US)
- Johns Hopkins Medicine (US)
- University of Chicago (US)
- The Ohio State University (US)
- University of Baltimore (US)
- Massachusetts Institute of Technology (US)
- University of Maryland, College Park (US)
Publication Details
- Journal
- Machine Learning Science and Technology
- Published
- 2026-01-27
- DOI
- https://doi.org/10.1088/2632-2153/ae3e4e
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00