The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples.

Ray, D., Trizio, E., Parrinello, M. (2023). Deep learning collective variables from transition path ensemble. THE JOURNAL OF CHEMICAL PHYSICS, 158(20) [10.1063/5.0148872].

Deep learning collective variables from transition path ensemble

Enrico Trizio;
2023

Abstract

The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples.
Articolo in rivista - Articolo scientifico
Collective Variables; Machine Learning; Enhanced Sampling
English
22-mag-2023
2023
158
20
204102
none
Ray, D., Trizio, E., Parrinello, M. (2023). Deep learning collective variables from transition path ensemble. THE JOURNAL OF CHEMICAL PHYSICS, 158(20) [10.1063/5.0148872].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/431579
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