Generative adversarial networks for transition state geometry prediction

Małgorzata Z Makoś; Niraj Verma; Eric C Larson; Marek Freindorf; Elfi Kraka

doi:10.1063/5.0055094

Generative adversarial networks for transition state geometry prediction

J Chem Phys. 2021 Jul 14;155(2):024116. doi: 10.1063/5.0055094.

Authors

Małgorzata Z Makoś¹, Niraj Verma¹, Eric C Larson², Marek Freindorf¹, Elfi Kraka¹

Affiliations

¹ Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA.
² Computer Science Department, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA.

PMID: 34266275
DOI: 10.1063/5.0055094

Abstract

This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN.