  Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in
use by computational chemists due to their combination of accuracy and speed.
Yet, it is still not clear how well these tools behave at or near transitions
states found in complex molecules. Here we investigate the applicability of
MLIPs in evaluating the transition barrier of two, complex, molecular motor
systems: a 1st generation Feringa motor and the 9c alkene 2nd generation
Feringa motor. We compared paths generated with the Hierarchically Interacting
Particle Neural Network (HIP-NN), the PM3 semi-empirical quantum method (SEQM),
PM3 interfaced with HIP-NN (SEQM+HIP-NN), and Density Functional Theory
calculations. We found that using SEQM+HIP-NN to generate cheap, realistic
pathway guesses then refining the intermediates with DFT allowed us to cheaply
find realistic reaction paths and energy barriers matching experiment,
providing evidence that deep learning can be used for high precision
computational tasks such as transition path sampling while also suggesting
potential application to high throughput screening.
