Dresden 2020 – scientific programme

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SYBD: Symposium Big data driven materials science

SYBD 1: Big Data Driven Materials Science

SYBD 1.5: Invited Talk

Tuesday, March 17, 2020, 11:45–12:15, HSZ 02

Deep learning of low-dimensional latent space molecular simulators — •Andrew Ferguson — Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637

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.