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

SUBMITTER: Ulanov E 

PROVIDER: S-EPMC11747964 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature