The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

• URL: http://arxiv.org/abs/2509.02661v1
• Date: Tue, 02 Sep 2025 18:00:00 GMT
• Title: The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
• Authors: Andrew Ferguson, Marisa LaFleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille Bilodeau, Keith Brown, Emmanuel Candes, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor Coley, Andrew Connolly, Fred Daum, Sijia Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gomez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutiérrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos Katsaggelos, Jeremy Kepner, Yannis Kevrekidis, Michelle Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, Rene Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang,
• Abstract summary: This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS)<n>We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025.<n>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.
• Score: 61.845407777089726
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Assessing Computer Science Student Attitudes Towards AI Ethics and Policy [8.927858368749204]
  The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest.<n>Despite computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied.
  arXiv  Detail & Related papers  (2025-04-06T23:03:47Z)
• Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? [20.94708392671015]
  We project that AI4Science's share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050.<n>We propose structured and actionable strategies to position AI researchers at the forefront of scientific discovery.
  arXiv  Detail & Related papers  (2025-03-05T09:29:05Z)
• KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model [8.538062912643424]
  We present our perspectives on how AI can better assist scientific inquiry and explore corresponding technical approach.<n>We have proposed and open-sourced two large models of our KALE-LM model series, KALE-LM-Chem(-1.5), which have achieved outstanding performance in tasks related to the field of chemistry.
  arXiv  Detail & Related papers  (2024-09-27T12:33:57Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• AI for social science and social science of AI: A Survey [47.5235291525383]
  Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research.
  arXiv  Detail & Related papers  (2024-01-22T10:57:09Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• Drivers and Barriers of AI Adoption and Use in Scientific Research [0.0]
  We study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions. Our results suggest that AI is pioneered by domain scientists with a taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers.
  arXiv  Detail & Related papers  (2023-12-15T14:49:13Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
  We estimate both the direct use of AI and the potential benefit of AI in scientific research. We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015. Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
  arXiv  Detail & Related papers  (2023-04-17T08:08:50Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.