用强化学习优化合金纳米颗粒的原子排列，一次训练可推广到不同尺寸。
Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles
- 基于几何图表示的强化学习代理，通过原子置换实现全局优化。
- 训练后能发现已知基态结构，且对初始构型不敏感。
- 可跨尺寸泛化，但多元素体系效果受限。
我们将双金属合金纳米颗粒（NPs）中最佳元素排列的搜索问题建模为强化学习（RL）任务，构建了一个利用纳米颗粒几何图表示进行全局优化的RL代理。为验证有效性，我们训练该代理在二十面体纳米颗粒结构上执行保持成分的原子交换操作。仅需一次对随机初始化的 $Ag_{X}Au_{309-X}$ 组成与排列的训练，代理即成功发现此前已确立的基态结构。结果显示，该优化方法对同一组成下不同的初始构型具有鲁棒性。此外，训练好的策略可有效外推至未见过尺寸的纳米颗粒。然而，在涉及多个合金元素时，其有效性受到限制。结果表明，结合预训练等变图编码的强化学习能够高效导航纳米尺度上的组合排列空间，并提供一种可迁移的优化策略，具备跨组成泛化潜力，有望降低重复搜索成本。
原文摘要 · Abstract (English)
We approach the search for optimal element ordering in bimetallic alloy nanoparticles (NPs) as a reinforcement learning (RL) problem and have built an RL agent that learns to perform such global optimization using the geometric graph representation of the NPs. To demonstrate the effectiveness, we train an RL agent to perform composition-conserving atomic swap actions on the icosahedral nanoparticle structure. Trained once on randomized $Ag_{X}Au_{309-X}$ compositions and orderings, the agent discovers previously established ground state structure. We show that this optimization is robust to differently ordered initialisations of the same NP compositions. We also demonstrate that a trained policy can extrapolate effectively to NPs of unseen size. However, the efficacy is limited when multiple alloying elements are involved. Our results demonstrate that RL with pre-trained equivariant graph encodings can navigate combinatorial ordering spaces at the nanoparticle scale, and offer a transferable optimization strategy with the potential to generalize across composition and reduce repeated individual search cost.
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