Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
This work addresses the computational bottleneck of slow molecular dynamics simulations for researchers in chemistry and biology, though it is incremental as it builds on existing diffusion models and graph neural networks.
The authors tackled the problem of accelerating molecular dynamics simulations by proposing score dynamics, a framework that uses conditional diffusion models to enable timesteps of 10 picoseconds, achieving a speedup of about two orders of magnitude compared to traditional methods while maintaining agreement in equilibrium and kinetic predictions.
We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular-dynamics simulations. SD is centered around scores, or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD timestep, which can be orders of magnitude larger than a typical MD timestep. In this work, we construct graph neural network based score dynamics models of realistic molecular systems that are evolved with 10~ps timesteps. We demonstrate the efficacy of score dynamics with case studies of alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about two orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve score dynamics are also discussed.