Neural Network Representations for Studying <scp>Gas‐Surface</scp> Reaction Dynamics: Beyond the <scp>Born‐Oppenheimer</scp> Static Surface Approximation<sup>†</sup>
Xueyao Zhou, Yaolong Zhang, Rongrong Yin, Ce Hu, Bin Jiang
Abstract
Abstract In the past a few years, there has been significant progress in theoretical characterizations of gas‐surface reaction dynamics at the atomic level. One of the major breakthroughs is the machine learning representations of the potential energy surfaces and related properties for molecules on metal surfaces from first‐principles, particularly neural networks based methods. In this review, we focus on recent advances of the development and applications of high‐dimensional symmetry‐preserving neural network representations in gas‐surface systems, which have enabled efficient Born‐Oppenheimer molecular dynamics simulations with inclusion of all molecular and surface degrees of freedom, as well as some nonadiabatic molecular dynamics simulations with effective treatment of hot electrons, at the density function theory level. Despite these advances, further challenges remain. More accurate electronic structure theories and more efficient machine learning (and active learning) algorithms are needed towards a more quantitative description of more complex gas‐surface reactions involving multiple surfaces and adsorbates or multiple electronic states.