Issue 2, 2025

Predicting hydrogen atom transfer energy barriers using Gaussian process regression

Abstract

Predicting reaction barriers for arbitrary configurations based on only a limited set of density functional theory (DFT) calculations would render the design of catalysts or the simulation of reactions within complex materials highly efficient. We here propose Gaussian process regression (GPR) as a method of choice if DFT calculations are limited to hundreds or thousands of barrier calculations. For the case of hydrogen atom transfer in proteins, an important reaction in chemistry and biology, we obtain a mean absolute error of 3.23 kcal mol−1 for the range of barriers in the data set using SOAP descriptors and similar values using the marginalized graph kernel. Thus, the two GPR models can robustly estimate reaction barriers within the large chemical and conformational space of proteins. Their predictive power is comparable to a graph neural network-based model, and GPR even outcompetes the latter in the low data regime. We propose GPR as a valuable tool for an approximate but data-efficient model of chemical reactivity in a complex and highly variable environment.

Graphical abstract: Predicting hydrogen atom transfer energy barriers using Gaussian process regression

Supplementary files

Article information

Article type
Paper
Submitted
24 Jun 2024
Accepted
06 Jan 2025
First published
10 Jan 2025
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2025,4, 513-522

Predicting hydrogen atom transfer energy barriers using Gaussian process regression

E. Ulanov, G. A. Qadir, K. Riedmiller, P. Friederich and F. Gräter, Digital Discovery, 2025, 4, 513 DOI: 10.1039/D4DD00174E

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