neural-potentials

This paper introduces a Hessian matching framework for machine-learned coarse-grained molecular dynamics that augments force matching with stochastic Hessian-vector product matching, instilling second-order curvature information into CG potentials. The method achieves up to 85% reduction in Kullback-Leibler divergence on slow-mode metrics for fast-folding proteins.