Differentiable Simulations for Enhanced Sampling of Rare Events | Martin Šípka

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Abstract: Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs). We propose using differentiable simulations (DiffSim) for the discovery and enhanced sampling of chemical transformations without a need to resort to preselected CVs, using only a distance metric. Reaction path discovery and estimation of the biasing potential that enhances the sampling are merged into a single end-to-end problem that is solved by path-integral optimization. This is achieved by introducing multiple improvements over standard DiffSim such as partial backpropagation and graph mini-batching making DiffSim training stable and efficient. The potential of DiffSim is demonstrated in the successful discovery of transition paths for the Muller-Brown model potential as well as a benchmark chemical system - alanine dipeptide.

Speaker: Martin Šípka -   / martinsipka  

Twitter Hannes:   / hannesstaerk  
Twitter Dominique:   / dom_beaini  
Twitter datamol.io:   / datamol_io  

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Chapters

00:00 - Intro
05:40 - Differentiable Simulations
11:41 - The Challenge of MD Simulation of Chemical Reactions
14:19 - Biased Langevin Dynamics
17:53 - 2D Case: Training
23:10 - Concave Surfaces
26:57 - Future Outlooks
31:19 - Q+A