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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2509.02661 |
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| author | Ferguson, Andrew LaFleur, Marisa Ruthotto, Lars Thaler, Jesse Ting, Yuan-Sen Tiwary, Pratyush Villar, Soledad Alves, E. Paulo Avigad, Jeremy Billinge, Simon Bilodeau, Camille Brown, Keith Candes, Emmanuel Chattopadhyay, Arghya Cheng, Bingqing Clausen, Jonathan Coley, Connor Connolly, Andrew Daum, Fred Dong, Sijia Du, Chrisy Xiyu Dvorkin, Cora Fanelli, Cristiano Ford, Eric B. Frutos, Luis Manuel Trillos, Nicolás García Garraffo, Cecilia Ghrist, Robert Gomez-Bombarelli, Rafael Guadagni, Gianluca Guggilam, Sreelekha Gukov, Sergei Gutiérrez, Juan B. Habib, Salman Hachmann, Johannes Hanin, Boris Harris, Philip Holland, Murray Holm, Elizabeth Huang, Hsin-Yuan Hsu, Shih-Chieh Jackson, Nick Isayev, Olexandr Ji, Heng Katsaggelos, Aggelos Kepner, Jeremy Kevrekidis, Yannis Kuchera, Michelle Kutz, J. Nathan Lalic, Branislava Lee, Ann LeBlanc, Matt Lim, Josiah Lindsey, Rebecca Liu, Yongmin Lu, Peter Y. Malik, Sudhir Mandic, Vuk Manian, Vidya Mazi, Emeka P. Mehta, Pankaj Melchior, Peter Ménard, Brice Ngadiuba, Jennifer Offner, Stella Olivetti, Elsa Ong, Shyue Ping Rackauckas, Christopher Rigollet, Philippe Risko, Chad Romero, Philip Rotskoff, Grant Savoie, Brett Seljak, Uros Shih, David Shiu, Gary Shlyakhtenko, Dima Silverstein, Eva Sparks, Taylor Strohmer, Thomas Stubbs, Christopher Thomas, Stephen Vaikuntanathan, Suriyanarayanan Vidal, Rene Villaescusa-Navarro, Francisco Voth, Gregory Wandelt, Benjamin Ward, Rachel Weber, Melanie Wechsler, Risa Whitelam, Stephen Wiest, Olaf Williams, Mike Yang, Zhuoran Yingling, Yaroslava G. Yu, Bin Yue, Shuwen Zabludoff, Ann Zhao, Huimin Zhang, Tong |
| author_facet | Ferguson, Andrew LaFleur, Marisa Ruthotto, Lars Thaler, Jesse Ting, Yuan-Sen Tiwary, Pratyush Villar, Soledad Alves, E. Paulo Avigad, Jeremy Billinge, Simon Bilodeau, Camille Brown, Keith Candes, Emmanuel Chattopadhyay, Arghya Cheng, Bingqing Clausen, Jonathan Coley, Connor Connolly, Andrew Daum, Fred Dong, Sijia Du, Chrisy Xiyu Dvorkin, Cora Fanelli, Cristiano Ford, Eric B. Frutos, Luis Manuel Trillos, Nicolás García Garraffo, Cecilia Ghrist, Robert Gomez-Bombarelli, Rafael Guadagni, Gianluca Guggilam, Sreelekha Gukov, Sergei Gutiérrez, Juan B. Habib, Salman Hachmann, Johannes Hanin, Boris Harris, Philip Holland, Murray Holm, Elizabeth Huang, Hsin-Yuan Hsu, Shih-Chieh Jackson, Nick Isayev, Olexandr Ji, Heng Katsaggelos, Aggelos Kepner, Jeremy Kevrekidis, Yannis Kuchera, Michelle Kutz, J. Nathan Lalic, Branislava Lee, Ann LeBlanc, Matt Lim, Josiah Lindsey, Rebecca Liu, Yongmin Lu, Peter Y. Malik, Sudhir Mandic, Vuk Manian, Vidya Mazi, Emeka P. Mehta, Pankaj Melchior, Peter Ménard, Brice Ngadiuba, Jennifer Offner, Stella Olivetti, Elsa Ong, Shyue Ping Rackauckas, Christopher Rigollet, Philippe Risko, Chad Romero, Philip Rotskoff, Grant Savoie, Brett Seljak, Uros Shih, David Shiu, Gary Shlyakhtenko, Dima Silverstein, Eva Sparks, Taylor Strohmer, Thomas Stubbs, Christopher Thomas, Stephen Vaikuntanathan, Suriyanarayanan Vidal, Rene Villaescusa-Navarro, Francisco Voth, Gregory Wandelt, Benjamin Ward, Rachel Weber, Melanie Wechsler, Risa Whitelam, Stephen Wiest, Olaf Williams, Mike Yang, Zhuoran Yingling, Yaroslava G. Yu, Bin Yue, Shuwen Zabludoff, Ann Zhao, Huimin Zhang, Tong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS) Ferguson, Andrew LaFleur, Marisa Ruthotto, Lars Thaler, Jesse Ting, Yuan-Sen Tiwary, Pratyush Villar, Soledad Alves, E. Paulo Avigad, Jeremy Billinge, Simon Bilodeau, Camille Brown, Keith Candes, Emmanuel Chattopadhyay, Arghya Cheng, Bingqing Clausen, Jonathan Coley, Connor Connolly, Andrew Daum, Fred Dong, Sijia Du, Chrisy Xiyu Dvorkin, Cora Fanelli, Cristiano Ford, Eric B. Frutos, Luis Manuel Trillos, Nicolás García Garraffo, Cecilia Ghrist, Robert Gomez-Bombarelli, Rafael Guadagni, Gianluca Guggilam, Sreelekha Gukov, Sergei Gutiérrez, Juan B. Habib, Salman Hachmann, Johannes Hanin, Boris Harris, Philip Holland, Murray Holm, Elizabeth Huang, Hsin-Yuan Hsu, Shih-Chieh Jackson, Nick Isayev, Olexandr Ji, Heng Katsaggelos, Aggelos Kepner, Jeremy Kevrekidis, Yannis Kuchera, Michelle Kutz, J. Nathan Lalic, Branislava Lee, Ann LeBlanc, Matt Lim, Josiah Lindsey, Rebecca Liu, Yongmin Lu, Peter Y. Malik, Sudhir Mandic, Vuk Manian, Vidya Mazi, Emeka P. Mehta, Pankaj Melchior, Peter Ménard, Brice Ngadiuba, Jennifer Offner, Stella Olivetti, Elsa Ong, Shyue Ping Rackauckas, Christopher Rigollet, Philippe Risko, Chad Romero, Philip Rotskoff, Grant Savoie, Brett Seljak, Uros Shih, David Shiu, Gary Shlyakhtenko, Dima Silverstein, Eva Sparks, Taylor Strohmer, Thomas Stubbs, Christopher Thomas, Stephen Vaikuntanathan, Suriyanarayanan Vidal, Rene Villaescusa-Navarro, Francisco Voth, Gregory Wandelt, Benjamin Ward, Rachel Weber, Melanie Wechsler, Risa Whitelam, Stephen Wiest, Olaf Williams, Mike Yang, Zhuoran Yingling, Yaroslava G. Yu, Bin Yue, Shuwen Zabludoff, Ann Zhao, Huimin Zhang, Tong Artificial Intelligence Instrumentation and Methods for Astrophysics Materials Science Machine Learning Data Analysis, Statistics and Probability 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. |
| title | The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS) |
| topic | Artificial Intelligence Instrumentation and Methods for Astrophysics Materials Science Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2509.02661 |