The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Abstract
This community paper developed out of the NSF 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.
Keywords
Cite
@article{arxiv.2509.02661,
title = {The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)},
author = {Andrew Ferguson and Marisa LaFleur and Lars Ruthotto and Jesse Thaler and Yuan-Sen Ting and Pratyush Tiwary and Soledad Villar and E. Paulo Alves and Jeremy Avigad and Simon Billinge and Camille Bilodeau and Keith Brown and Emmanuel Candes and Arghya Chattopadhyay and Bingqing Cheng and Jonathan Clausen and Connor Coley and Andrew Connolly and Fred Daum and Sijia Dong and Chrisy Xiyu Du and Cora Dvorkin and Cristiano Fanelli and Eric B. Ford and Luis Manuel Frutos and Nicolás García Trillos and Cecilia Garraffo and Robert Ghrist and Rafael Gomez-Bombarelli and Gianluca Guadagni and Sreelekha Guggilam and Sergei Gukov and Juan B. Gutiérrez and Salman Habib and Johannes Hachmann and Boris Hanin and Philip Harris and Murray Holland and Elizabeth Holm and Hsin-Yuan Huang and Shih-Chieh Hsu and Nick Jackson and Olexandr Isayev and Heng Ji and Aggelos Katsaggelos and Jeremy Kepner and Yannis Kevrekidis and Michelle Kuchera and J. Nathan Kutz and Branislava Lalic and Ann Lee and Matt LeBlanc and Josiah Lim and Rebecca Lindsey and Yongmin Liu and Peter Y. Lu and Sudhir Malik and Vuk Mandic and Vidya Manian and Emeka P. Mazi and Pankaj Mehta and Peter Melchior and Brice Ménard and Jennifer Ngadiuba and Stella Offner and Elsa Olivetti and Shyue Ping Ong and Christopher Rackauckas and Philippe Rigollet and Chad Risko and Philip Romero and Grant Rotskoff and Brett Savoie and Uros Seljak and David Shih and Gary Shiu and Dima Shlyakhtenko and Eva Silverstein and Taylor Sparks and Thomas Strohmer and Christopher Stubbs and Stephen Thomas and Suriyanarayanan Vaikuntanathan and Rene Vidal and Francisco Villaescusa-Navarro and Gregory Voth and Benjamin Wandelt and Rachel Ward and Melanie Weber and Risa Wechsler and Stephen Whitelam and Olaf Wiest and Mike Williams and Zhuoran Yang and Yaroslava G. Yingling and Bin Yu and Shuwen Yue and Ann Zabludoff and Huimin Zhao and Tong Zhang},
journal= {arXiv preprint arXiv:2509.02661},
year = {2026}
}
Comments
Community Paper from the NSF Future of AI+MPS Workshop, Cambridge, Massachusetts, March 24-26, 2025, supported by NSF Award Number 2512945; v2: minor clarifications; v3: approximate version to appear in MLST