标题： 多智能体强化学习在晶体结构优化中的应用 显示英文标题

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

摘要： 原子结构的几何优化是计算化学和材料设计中的常见且关键任务。 遵循学习优化范式，我们提出了一种新的多智能体强化学习方法，称为多智能体晶体结构优化（MACS），用于解决周期性晶体结构优化问题。 MACS 将几何优化视为一个部分可观察的马尔可夫博弈，在该博弈中，原子作为智能体调整它们的位置，以共同发现一个稳定的构型。 我们在各种已报道的晶体材料组成上训练 MACS，以获得一种策略，成功优化来自训练组成的结构以及更大尺寸和未见过组成的结构，验证了其出色的可扩展性和零样本迁移能力。 我们将我们的方法与一系列最先进的优化方法进行基准测试，并证明 MACS 显著加快了周期性晶体结构的优化速度，所需的能量计算更少，并且失败率最低。 显示英文摘要

摘要： 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.