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Simulation of surface processes is a key part of computational chemistry thatoffers atomic-scale insights into mechanisms of heterogeneous catalysis,diffusion dynamics, as well as quantum tunneling phenomena. The most commontheoretical approaches involve optimization of reaction pathways, includingsemiclassical tunneling pathways (called instantons). However, thecomputational effort can be demanding, especially for instanton optimizationswith ab initio electronic structure. Recently, machine learning has beenapplied to accelerate reaction-pathway optimization, showing great potentialfor a wide range of applications. However, previous methods suffer frompractical issues such as unfavorable scaling with respect to the size of thedescriptor, and were mostly designed for reactions in the gas phase. We proposean improved framework based on Gaussian process regression for generaltransformed coordinates, which can alleviate the size problem. The descriptorcombines internal and Cartesian coordinates, which improves the performance formodeling surface processes. We demonstrate with eleven instanton optimizationsin three example systems that the new approach makes ab initio instantonoptimization significantly cheaper, such that it becomes not much moreexpensive than a classical transition-state theory calculation.

Robust Gaussian Process Regression method for efficient reaction pathway optimization: application to surface processes