Application of neural network potentials to modelling transition states

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Urquhart, Ross James and van Teijlingen, Alexander and Tuttle, Tell (2025) Application of neural network potentials to modelling transition states. Chemical Communications, 61 (63). pp. 11810-11813. ISSN 1364-548X (https://doi.org/10.1039/d5cc02090e)

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Abstract

Transition state modelling remains a challenge in computational chemistry, often requiring chemical intuition and expensive, iterative recalculations. This work presents a more efficient approach using umbrella sampling to explore free energy surface and more importantly, the conformational space around transition states, reducing the effort needed for structure identification. By employing a machine learning potential, ANI-2x, [C. Devereux et al., J. Chem. Theory Comput., 2020, 16, 4192–4202] to drive the sampling, we demonstrate enhanced FES exploration and efficiency compared to traditional DFT methods. The approach is applied to two different reactions: amide formation via a thioester intermediate and disulphide bridge formation. It was found that ANI-2x performs poorly at the prediction of high energy structures yet provides rapid, thorough sampling of reaction pathways making it useful for informing further calculations at higher levels of theory.

ORCID iDs

Urquhart, Ross James ORCID logoORCID: https://orcid.org/0000-0001-8505-2798, van Teijlingen, Alexander ORCID logoORCID: https://orcid.org/0000-0002-3739-8943 and Tuttle, Tell ORCID logoORCID: https://orcid.org/0000-0003-2300-8921;
CORE (COnnecting REpositories)