Title: Understanding Long Timescale Phenomena in Chemical and Soft Matter Simulations with Variationally Enhanced Sampling

Speaker: Omar Valsson (Max Planck Inst Polymer Research)


The usefulness of atomistic simulations is generally hampered by the presence of several metastable states separated by high barriers leading to kinetic bottlenecks. Transitions between meta-stable states thus occur on much longer time scales than one can simulate in practice. Numerous enhanced sampling methods have been introduced to alleviate this time scale problem, including methods based on identifying a few crucial order parameters (generally called collective variables) and enhancing their sampling by introducing an external biasing potential [1].

Variationally Enhanced Sampling (VES) [2,3] is one such enhanced sampling method that is based on a variational principle where an external bias potential is constructed by minimizing a convex functional. The technique is generally applicable where one can define suitable collective variables and allows to obtain both free energy landscapes and kinetics of rare events.

In this talk, I will present two new developments of VES. The first development is the implementation of wavelet-based bias potentials that gives better performance than localized basis functions. The second development is an extension for biasing permutationally invariant local collective variables. We show how we can use this extension to accelerate phase-transitions in materials composed of identical building blocks.

I will also present a machine learning method called multiscale reweighted stochastic embedding (MRSE) [4] for automatically constructing collective variables to represent and drive the sampling of free energy landscapes in enhanced sampling simulations.

[1] O. Valsson, P. Tiwary, and M. Parrinello, Annu. Rev. Phys. Chem. 67 159-184 (2016) [doi: 10.1146/annurev-physchem-040215-112229]
[2] O. Valsson and M. Parrinello, Phys. Rev. Lett. 113 090601 (2014) [doi: 10.1103/PhysRevLett.113.090601]
[3] O. Valsson and M. Parrinello, Handbook of Materials Modeling, Methods: Theory and Modeling (Vol. I) [doi: 10.1007/978-3-319-42913-7_50-1]
[4] J. Rydzewski, and O. Valsson, arXiv:2007.06377.

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