Learning to Optimize Molecular Geometries Using Reinforcement Learning

Though quasi-Newton methods have
been widely adopted in computational
chemistry software for molecular geometry optimization, it is well
known that these methods might not perform well for initial guess
geometries far away from the local minima, where the quadratic approximation
might be inaccurate. We propose a reinforcement learning approach
to develop a model that produces a correction term for the quasi-Newton
step calculated with the BFGS algorithm to improve the overall optimization
performance. Our model is able to complete the optimization in about
30% fewer steps than pure BFGS for molecules starting from perturbed
geometries. The new method has similar convergence to BFGS when complemented
with a line search procedure, but it is much faster since it avoids
the multiple gradient evaluations associated with line searches.