(409h) Deep Learning of Low-Dimensional Latent Space Molecular Simulators
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Catalysis and Reaction Engineering Division
In Honor of Yannis Kevrekidis' 60th Birthday (Invited Talks)
Tuesday, November 12, 2019 - 5:43pm to 6:02pm
The long-time microscopic evolution of molecular systems is governed by the leading eigenfunctions of the transfer operator that propagates the system dynamics through time. The low-dimensional latent space defined by these eigenfunctions parameterize the slow manifold to which the system dynamics are constrained to evolve. A set of three deep neural networks of different architectures trained over short molecular simulation trajectories provides a means to (i) learn the leading transfer operator eigenfunctions, (ii) propagate the dynamics within the encoded latent space, and (iii) decode the latent space back to the all-atom coordinate space. This technique offers a means to train numerical simulators to conduct molecular simulations and estimate thermodynamic and kinetic observables at orders-of-magnitude lower cost than conventional molecular dynamics calculations.