Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes

doi:10.1021/acs.jctc.4c00158

Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes

2024-05-06

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Jeremy O. Richardson *

DOI：10.1021/acs.jctc.4c00158

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Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, and quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). The computational effort can be demanding, especially for instanton optimizations with an ab initio electronic structure. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods still suffer from numerical and efficiency issues and were not designed for condensed-phase reactions. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which has improved efficiency and numerical stability, and we propose a descriptor that combines internal and Cartesian coordinates suitable for modeling surface processes. We demonstrate with 11 instanton optimizations in three representative systems that the improved approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory rate calculation.

Keywords:

INITIO MOLECULAR-DYNAMICS

FINDING SADDLE-POINTS

GEOMETRY OPTIMIZATION

TRANSITION

SIMULATION

MATRIX

WATER

Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes

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