能量驱动的结构匹配用于自主全X射线散射实验

中文摘要 AI 辅助

自主实验室的出现促使实验数据在闭环优化兼容的时间尺度上快速转化为可靠的原子模型。在此，我们开发了一个能量驱动的结构匹配框架，用于正在进行的全X射线散射实验期间的分析。利用金纳米颗粒的数据，我们匹配了理想化的球形、八面体、十面体和二十面体几何形状，它们的机器学习原子间势（MLIP）弛豫结构，以及分子动力学（MD）系综。理想化模型生成快速，但可能错误分配形貌，并通过忽略表面弛豫、应变和热无序而系统性低估尺寸。MLIP弛豫显著改善了这两方面，而MD系综平均与实验最吻合。因此，我们引入了一个分层工作流程，将快速理想化筛选与针对顶级候选的MLIP和MD细化相结合，在不中断自主操作的情况下提供改进的结构反馈。该框架为自主实验活动提供了一条途径，其中目标结构本身可以根据与实验数据兼容的结构的演化能量景观进行更新。

英文摘要

The emergence of autonomous laboratories motivates rapid conversion of experimental data into reliable atomistic models on time-scales compatible with closed-loop optimization. Here we develop an energetically driven structure matching framework for analysis during ongoing total X-ray scattering experiments. Using data from gold nanoparticles, we match against idealized spherical, octahedral, decahedral, and icosahedral geometries, their machine-learned interatomic potential (MLIP)-relaxed structures, and molecular dynamics (MD) ensembles. Idealized models are fast to generate but can misassign morphology and systematically underestimate size by neglecting surface relaxation, strain, and thermal disorder. MLIP relaxation markedly improves both, while MD ensemble averaging agrees best with experiment. We therefore introduce a hierarchical workflow combining rapid idealized screening with targeted MLIP and MD refinement of top candidates, delivering improved structural feedback without interrupting autonomous operation. This framework provides a route towards autonomous campaigns in which the target structure itself can be updated in response to the evolving energy landscape of structures compatible with the experimental data.

发表机构

• Technical University of Denmark (DTU)（丹麦技术大学）
• IT University of Copenhagen（哥本哈根信息技术大学）
• Aarhus University（奥胡斯大学）

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