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
, et al. (75 additional authors not shown)
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…
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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.
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Submitted 15 March, 2026; v1 submitted 2 September, 2025;
originally announced September 2025.
Simulating charging characteristics of lithium iron phosphate by electro-ionic optimization on a quantum annealer
Authors:
Tobias Binninger,
Yin-Ying Ting,
Konstantin Köster,
Nils Bruch,
Payam Kaghazchi,
Piotr M. Kowalski,
Michael H. Eikerling
Abstract:
The rapid evolution of quantum computing hardware opens up new avenues in the simulation of energy materials. Today's quantum annealers are able to tackle complex combinatorial optimization problems. A formidable challenge of this type is posed by materials with site-occupational disorder for which atomic arrangements with a low, or lowest, energy must be found. In this article, a method is presen…
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The rapid evolution of quantum computing hardware opens up new avenues in the simulation of energy materials. Today's quantum annealers are able to tackle complex combinatorial optimization problems. A formidable challenge of this type is posed by materials with site-occupational disorder for which atomic arrangements with a low, or lowest, energy must be found. In this article, a method is presented for the identification of the correlated ground-state distribution of both lithium ions and redox electrons in lithium iron phosphate (LFP), a widely employed cathode material in lithium-ion batteries. The point-charge Coulomb energy model employed correctly reproduces the LFP charging characteristics. As is shown, grand-canonical transformation of the energy cost function makes the combinatorial distribution problem solvable on quantum annealing (QA) hardware. The QA output statistics follow a pseudo-thermal behavior characterized by a problem-dependent effective sampling temperature, which has bearings on the estimated scaling of the QA performance with system size. This work demonstrates the potential of quantum computation for the joint optimization of electronic and ionic degrees of freedom in energy materials.
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Submitted 13 March, 2025;
originally announced March 2025.
Optimization of ionic configurations in battery materials by quantum annealing
Authors:
Tobias Binninger,
Yin-Ying Ting,
Piotr M. Kowalski,
Michael H. Eikerling
Abstract:
Energy materials with disorder in site occupation are challenging for computational studies due to an exponential scaling of the configuration space. We herein present a grand-canonical optimization method that enables the use of quantum annealing (QA) for sampling the ionic ground state. The method relies on a Legendre transformation of the Coulomb energy cost function that strongly reduces the e…
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Energy materials with disorder in site occupation are challenging for computational studies due to an exponential scaling of the configuration space. We herein present a grand-canonical optimization method that enables the use of quantum annealing (QA) for sampling the ionic ground state. The method relies on a Legendre transformation of the Coulomb energy cost function that strongly reduces the effective coupling strengths of the fully connected problem, which is essential for effectiveness of QA. The approach is expected to be applicable to a variety of materials optimization problems.
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Submitted 26 November, 2024; v1 submitted 4 January, 2024;
originally announced January 2024.