Differentiable Simulations for Enhanced Sampling of Rare Events | Martin Šípka

Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: https://datamol.io/ Never miss another LoGG Talk, add the schedule to your calendar: https://m2d2.io/talks/m2d2/about/ Join the Learning on Graphs and Geometry Reading Group on Slack: https://join.slack.com/t/logag/shared... 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   ~ 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