Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling

Journal of Chemical Physics

In this paper we accelerate HMC simulations by learning atom-dependent timesteps and the number of molecular dynamics steps! With an appropriate choice of the loss, we can significantly speed up simulations and reduce autocorrelation times by 25% on alanine dipeptide.

Federico Errica
Federico Errica
Research Scientist

My research interests include distributed robotics, mobile computing and programmable matter.