Application of neural network potentials to modelling transition states

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.