Ab initio instanton rate theory made efficient using Gaussian process regression

TL;DR

This paper introduces a Gaussian process regression method to efficiently compute ab initio instanton rates, significantly reducing computational cost while maintaining accuracy for tunnelling-influenced chemical reaction rates.

Contribution

It presents a novel local fitting approach using Gaussian process regression to accelerate ab initio instanton calculations for reaction rates.

Findings

Fast convergence to benchmark H + CH4 results

Accurate low-temperature rates for H + C2H6

Reduced number of electronic-structure calculations

Abstract

Ab initio instanton rate theory is a computational method for rigorously including tunnelling effects into calculations of chemical reaction rates based on a potential-energy surface computed on the fly from electronic-structure theory. This approach is necessary to extend conventional transition-state theory into the deep-tunnelling regime, but is also more computationally expensive as it requires many more ab initio calculations. We propose an approach which uses Gaussian process regression to fit the potential-energy surface locally around the dominant tunnelling pathway. The method can be converged to give the same result as from an on-the-fly ab initio instanton calculation but requires far fewer electronic-structure calculations. This makes it a practical approach for obtaining accurate rate constants based on high-level electronic-structure methods. We show fast convergence to reproduce benchmark H + CH4 results and evaluate new low-temperature rates of H + C2H6 in full dimensionality at a UCCSD(T)-F12b/cc-pVTZ-F12 level.