Molecular Latent Space Simulators

We have developed molecular latent space simulators (LSS) to learn highly efficient and accurate surrogate models of molecular dynamics (MD) by stacking three specialized deep learning networks to (i) encode a molecular system into a slow latent space, (ii) propagate dynamics in this latent space, and (iii) generatively decode a synthetic molecular trajectory. The trained LSS generates novel ultra-long molecular trajectories at six orders of magnitude lower cost than MD enabling resolution of rare thermodynamic states and kinetic transitions with arbitrarily low statistical uncertainties. In an application to Trp-cage, we generate millisecond trajectories in just minutes of wall clock time and demonstrate excellent agreement with the MD structure, thermodynamics, and kinetics.

H. Sidky, W. Chen, and A.L. Ferguson Chem. Sci. 11 9459 (2020)