High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas-Surface Scattering Processes from Machine Learning.,The Journal of Physical Chemistry Letters

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High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas-Surface Scattering Processes from Machine Learning.
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2020-06-09 , DOI: 10.1021/acs.jpclett.0c00989
Bin Jiang ₁ , Jun Li ₂ , Hua Guo ₃

Affiliation

In this Perspective, we review recent advances in constructing high-fidelity potential energy surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs, albeit with substantial initial investments, provide significantly higher efficiency than direct dynamics methods and/or high accuracy at a level that is not affordable by on-the-fly approaches. These PESs not only are a necessity for quantum dynamical studies because of delocalization of wave packets but also enable the study of low-probability and long-time events in (quasi-)classical treatments. Our focus here is on inelastic and reactive scattering processes, which are more challenging than bound systems because of the involvement of continua. Relevant applications and developments for dynamical processes in both the gas phase and at gas–surface interfaces are discussed.

中文翻译：

机器学习中用于气相和气相表面散射过程的高保真势能面。

在此观点中，我们回顾了从离散的从头算构造高保真势能面（PES）的最新进展。点，使用机器学习工具。尽管具有大量的初期投资，但这种PES的效率要比直接动力学方法高得多，并且/或者在实时方法无法承受的水平上具有很高的准确性。这些PES不仅由于波包的局域化而成为进行量子动力学研究的必要条件，而且还使研究（准）经典治疗中的低概率和长期事件成为可能。我们的重点是非弹性和反应性散射过程，由于连续性的参与，它们比约束系统更具挑战性。讨论了气相和气相-表面界面动力学过程的相关应用和发展。

更新日期：2020-07-02

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