Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems
Commentary byAlessandro Coretti1; Sebastian Falkner1; Jan Weinreich1; Christoph Dellago1; O. Anatole von Lilienfeld2
1Faculty of Physics, University of Vienna, Austria
2Vector Institute and Departments of Chemistry and Materials Science and Engineering, University of Toronto, Canada
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
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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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