Nuclear gradients from auxiliary-field quantum Monte Carlo and their application in geometry optimization and transition state search
This paper presents an efficient method for computing accurate nuclear forces within the phaseless auxiliary-field quantum Monte Carlo framework using automatic differentiation, which is then combined with machine learning potentials to successfully perform geometry optimizations and transition state searches that agree closely with coupled-cluster reference values.