Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions.

ABSTRACT: Ring strain energy (RSE) is crucial for understanding molecular reactivity, with broad implications in polymerization, click chemistry, drug discovery and beyond. However, quantitatively determining RSE through experiments or quantum mechanics (QM) is resource-intensive, limiting its application on a large scale. We present a machine learning (ML)-based workflow that enables the reliable and efficient prediction of RSE, entirely bypassing traditional QM calculations. Our workflow employs AIMNet2 machine learning interatomic potentials and Auto3D for the identification of low-energy conformers and RSE computation. Remarkably, it achieves an R ² of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol when benchmarked against the ωB97M-D4/Def2-TZVPP method, while running

SUBMITTER: Liu Z 

PROVIDER: S-EPMC12569667 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature