Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials

Transition states are a critical bottleneck in chemical transformations. Significant efforts have been made to develop algorithms that efficiently locate transition states on potential energy surfaces. However, the computational cost of ab-initio potential energy surface evaluation limits the size of chemical systems that can routinely studied. In this work, we develop and fine-tune a graph neural network potential energy function suitable for describing organic chemical reactions and use it to rapidly identify transition state guess structures. We successfully refine guess structures and locate a transition state in each test system considered and reduce the average number of ab-initio calculations by 47% though use of the graph neural network potential energy function. Our results show that modern machine learning models have reached levels of reliability whereby they can be used to accelerate routine computational chemistry tasks.