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Updated: Sep 27, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Conformational Sampling for Transition State Searches on a Computational Budget.

Qiyuan Zhao¹, Hsuan-Hao Hsu¹, Brett M Savoie¹

¹Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.

Journal of Chemical Theory and Computation

|April 11, 2022

Summary

Machine learning classifiers can identify optimal reaction conformers for transition state searches, significantly reducing computational costs and improving accuracy in chemical reaction mechanism characterization.

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Area of Science:

Computational Chemistry
Chemical Reaction Mechanisms
Machine Learning Applications

Background:

Transition state searches are crucial for understanding chemical reactions but are computationally expensive.
Current methods struggle with the vast conformational spaces of reacting systems, limiting accuracy.

Purpose of the Study:

To develop a machine learning approach for down-selecting optimal reaction conformers prior to computationally intensive transition state searches.
To improve the efficiency and accuracy of chemical reaction mechanism characterization.

Main Methods:

Training a classifier to recognize features of conformers conducive to successful transition state searches.
Testing the approach on four benchmarks with over 300 reactions using random forest models.

Main Results:

Machine learning classifiers reliably identified low-barrier conformers for unseen reactions.
Ignoring conformer contributions led to inaccurate activation energy estimations across various reactions.

Conclusions:

This machine learning strategy effectively reduces the cost of conformational sampling in reaction prediction.
The approach enhances the feasibility of incorporating conformational analysis into computational workflows, paving the way for future advancements.