用机器学习力场模拟原子薄膜过程，揭示界面反应机制。
On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields
- 基于机器学习构建适合原子尺度模拟的力场。
- 成功应用于HfO2沉积与MoS2刻蚀过程模拟。
- 为工业相关薄膜工艺提供可解释的原子级洞察。
原子尺度建模为揭示薄膜过程中的关键化学机理以及获取气-表面界面事件与反应的定量指标提供了途径。分子动力学（MD）是研究原子尺度过程演化的强大计算方法，但工业相关过程通常需要合适的力场，而这些力场在大多数情况下尚未可用。然而，机器学习力场（MLFF）正迅速成为计算材料与表面科学领域的主流。本文展示了高效构建适用于过程模拟的MLFF的方法，并以两个技术相关的例子进行验证：HfO2原子层沉积中的前驱体脉冲过程和MoS2原子层刻蚀过程。
原文摘要 · Abstract (English)
Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-surface interface. Molecular dynamics (MD) is a powerful computational method to study the evolution of a process at the atomic scale, but studies of industrially relevant processes usually require suitable force fields, which are in general not available for all processes of interest. However, machine learned force fields (MLFF) are conquering the field of computational materials and surface science. In this paper, we demonstrate how to efficiently build MLFFs suitable for process simulations and provide two examples for technologically relevant processes: precursor pulse in the atomic layer deposition of HfO2 and atomic layer etching of MoS2.
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