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Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems

Alessandro Coretti¹; Sebastian Falkner¹; Jan Weinreich¹; Christoph Dellago¹; O. Anatole von Lilienfeld^2
1Faculty of Physics, University of Vienna, Austria
²Vector Institute and Departments of Chemistry and Materials Science and Engineering, University of Toronto, Canada

Commentary on

F. Noé, S. Olsson, J. Köhler and H. Wu, “Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning”, Science, 365:6457 (2019), https://doi.org/10.1126/science.aaw1147

Read the Commentary (PDF)

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Statement of Significance

The paper by Noé et al. (Science, 2021) introduced the concept of Boltzmann Generators (BGs), a deep generative model that can produce unbiased independent samples of many-body systems. They can generate equilibrium configurations from different metastable states, compute relative stabilities between different structures of proteins or other organic molecules, and discover new states. In this commentary, we motivate the necessity for a new generation of sampling methods beyond molecular dynamics, explain the methodology, and give our perspective on the future role of BGs.

How to cite

Cite the commentary as:

A. Coretti, S. Falkner, J. Weinreich, C. Dellago and O.A. von Lilienfeld, "Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems",
KIM REVIEW, Volume 2, Article 03, 2024. DOI: 10.25950/bfa99422

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