MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Article Swipe

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Elena Zamaraeva , Christopher M. Collins , George R. Darling , Matthew S. Dyer , Bei Peng , Rahul Savani , Dmytro Antypov , Vladimir V. Gusev , Judith Clymo , Paul G. Spirakis , Matthew J. Rosseinsky ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2506.04195

Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a new multi-agent reinforcement learning method called Multi-Agent Crystal Structure optimization (MACS) to address periodic crystal structure optimization. MACS treats geometry optimization as a partially observable Markov game in which atoms are agents that adjust their positions to collectively discover a stable configuration. We train MACS across various compositions of reported crystalline materials to obtain a policy that successfully optimizes structures from the training compositions as well as structures of larger sizes and unseen compositions, confirming its excellent scalability and zero-shot transferability. We benchmark our approach against a broad range of state-of-the-art optimization methods and demonstrate that MACS optimizes periodic crystal structures significantly faster, with fewer energy calculations, and the lowest failure rate.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2506.04195
PDF: https://arxiv.org/pdf/2506.04195
OA Status: green
OpenAlex ID: https://openalex.org/W4416075339

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OpenAlex ID: https://openalex.org/W4416075339

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DOI: https://doi.org/10.48550/arxiv.2506.04195

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Title: MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-06-04

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Authors: Elena Zamaraeva, Christopher M. Collins, George R. Darling, Matthew S. Dyer, Bei Peng, Rahul Savani, Dmytro Antypov, Vladimir V. Gusev, Judith Clymo, Paul G. Spirakis, Matthew J. Rosseinsky

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Landing page: https://arxiv.org/abs/2506.04195

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PDF URL: https://arxiv.org/pdf/2506.04195

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Cited by: 0

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