用多智能体强化学习优化晶体结构，速度快且能零样本迁移。
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
- 将原子视为智能体，通过协作寻找稳定晶体构型。
- 相比主流方法，能耗计算次数更少，失败率最低，速度提升显著。
- 适用于新成分和更大结构，具备强泛化能力，适合材料设计场景。
原子结构的几何优化是计算化学与材料设计中的常见且关键任务。本文提出一种名为多智能体晶体结构优化（MACS）的新方法，基于‘学会优化’范式，解决周期性晶体结构的优化问题。MACS 将几何优化建模为部分可观测马尔可夫博弈，其中原子作为智能体协同调整位置以发现稳定构型。我们在多种已报道晶体材料的成分上训练 MACS，结果表明该策略不仅能有效优化训练成分的结构，还可成功应用于更大尺寸及未见成分的结构，验证了其出色的可扩展性和零样本迁移能力。在与多种前沿优化方法的对比中，MACS 显著加快了周期性晶体结构的优化速度，减少能量计算次数，并达到最低失败率。
原文摘要 · Abstract (English)
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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