Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

ABSTRACT: Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which

SUBMITTER: Chang HC 

PROVIDER: S-EPMC11815826 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature