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

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 R2 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 orders
of magnitude faster than DFT calculations. To demonstrate the utility
of our workflow, we successfully differentiated reactive from nonreactive
molecules in copper-free click chemistry, [3 + 2] cycloaddition reaction
and ring-opening metathesis polymerization, underscoring its transferability
to diverse molecular systems. Additionally, we compiled the RSE Atlas,
a computational database encompassing 16,905 single-ring molecules,
offering a valuable resource for investigating factors influencing
RSE. Our approach transforms RSE into a readily computable property,
facilitating its integration into reaction designs.