CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network.

ABSTRACT: Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density functional theory) can be time-consuming and computationally expensive. In this work, we introduce CoeffNet, an equivariant graph neural network that predicts activation barriers using coefficients of any frontier molecular orbital (such as the highest occupied molecular orbital) of reactant and product complexes as graph node features. We show that using coefficients as features offer several advantages, such as chemical interpretability and physical constraints on the network's behaviour and numerical range. Model outputs are either activatio

SUBMITTER: Vijay S 

PROVIDER: S-EPMC10882514 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature