Δ² machine learning for reaction property prediction.

ABSTRACT: The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ²-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ² model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC10686042 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature