Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
Zhen Liu, Jessica Vinskus, Yue Fu, Peng Liu, Kevin Noonan, Olexandr Isayev
Highlight
Ring strain energy (RSE) is crucial for understanding molecular reactivity.
Abstract
Ring strain energy (RSE) is crucial for understanding molecular reactivity. However, quantitatively determining RSE through experiments or quantum mechanics is resource-intensive, limiting its application on a large scale. We developed a physics-based workflow and a data-driven graph neural network (GNN) capable of reliably predicting RSE in minutes or milliseconds, respectively. For each molecule, the workflow first identifies low-energy conformers, then computes the RSE using the AIMNet2 machine learning interatomic potentials. We validated the approach both computationally and experimentally. Compared to the ωB97M-D4/Def2-TZVPP method, the workflow achieved an R² value of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol. Using this workflow, we distinguished reactive from non-reactive molecules in copper-free click chemistry and ring-opening metathesis polymerization, demonstrating the workflow's generalizability to diverse molecules. Furthermore, we compiled "RSE Atlas," a computational database of 16,905 single-ring molecules, providing a rich resource for examining factors influencing RSE. Employing this dataset, we trained a GNN that predicts RSE in milliseconds using only 2D molecular information. Our methods render RSE a readily computable property for on-the-fly applications in experimental and computational work.
Keywords
Cite This Paper
@article{Liu2024,
author = {Liu, Zhen and Vinskus, Jessica and Fu, Yue and Liu, Peng and Noonan, Kevin and Isayev, Olexandr},
title = {Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions},
year = {2024},
doi = {10.26434/chemrxiv-2024-dtq6q},
url = {http://dx.doi.org/10.26434/chemrxiv-2024-dtq6q},
publisher = {American Chemical Society (ACS)},
keywords = {neural network, machine learning, graph neural},
researchAreas = {ml-potentials, experiment-automation, reactions-reactivity},
highlight = {Ring strain energy (RSE) is crucial for understanding molecular reactivity.},
citations = {2}
} Copied to clipboard!
Related Research Areas
Related Publications
Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
(2025)
Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
(2023)
Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie International Edition (2025)
Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.
Democratizing Reaction Kinetics through Machine Vision and Learning
(2025)
Democratizing Reaction Kinetics through Machine Vision and Learning.