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Predictive collective variable discovery with deep Bayesian models.

Markus Schöberl1, Nicholas Zabaras1, Phaedon-Stelios Koutsourelakis2

  • 1Center for Informatics and Computational Science, University of Notre Dame, 311 Cushing Hall, Notre Dame, Indiana 46556, USA.

The Journal of Chemical Physics
|January 17, 2019
PubMed
Summary
This summary is machine-generated.

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We introduce a Bayesian inference approach to discover crucial collective variables (CVs) for enhanced sampling in complex molecular simulations. This method accelerates exploration of atomistic systems and improves understanding of underlying mechanisms.

Area of Science:

  • Computational Chemistry and Physics
  • Biochemistry
  • Materials Science

Background:

  • Complex atomistic systems in biochemistry and materials science face spatio-temporal scale limitations in current models.
  • Enhanced sampling methods are crucial for overcoming these limitations, but their efficiency relies heavily on the selection of collective variables (CVs).

Purpose of the Study:

  • To develop a novel method for discovering effective CVs.
  • To formulate CV discovery as a Bayesian inference problem, treating CVs as hidden generators of atomistic trajectories.
  • To enable accurate estimation of observables and their uncertainties from limited data.

Main Methods:

  • Formulation of CV discovery as a Bayesian inference problem.
  • Utilizing machine learning and variational inference techniques.

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  • Treating CVs as hidden generators of full-atomistic trajectories.

    • Main Results:

    • The methodology generates samples of fine-scale atomistic configurations from limited training data.
    • Discovered CVs correlate with essential physicochemical properties.
    • Quantitative assessment demonstrates the predictive ability of the discovered CVs for alanine dipeptide (ALA-2) and ALA-15 peptide.

    Conclusions:

    • The Bayesian inference approach effectively discovers relevant CVs for enhanced sampling.
    • This method enhances the exploration of configurational space in complex molecular systems.
    • The discovered CVs provide insights into mechanisms, particularly in unexplored scientific domains.