Analyzing Multimodal Probability Measures with Autoencoders.

Finding collective variables to describe some important coarse-grained information on physical systems, in particular metastable states, remains a key issue in molecular dynamics. Recently, machine learning techniques have been intensively used to complement and possibly bypass expert knowledge in order to construct collective variables. Our focus here is on neural network approaches based on autoencoders.

Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Molecular Dynamics.

Computing accurate rate constants for catalytic events occurring at the surface of a given material represents a challenging task with multiple potential applications in chemistry. To address this question, we propose an approach based on a combination of the rare event sampling method called adaptive multilevel splitting (AMS) and molecular dynamics. The AMS method requires a one-dimensional reaction coordinate to index the progress of the transition.