Normalizing flows as an enhanced sampling method for atomistic supercooled liquids - Ecole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris - PSL
Article Dans Une Revue Machine Learning: Science and Technology Année : 2024

Normalizing flows as an enhanced sampling method for atomistic supercooled liquids

Résumé

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement.
Fichier principal
Vignette du fichier
2404.09914v2.pdf (1.78 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04699130 , version 1 (16-09-2024)

Licence

Identifiants

Citer

Gerhard Jung, Giulio Biroli, Ludovic Berthier. Normalizing flows as an enhanced sampling method for atomistic supercooled liquids. Machine Learning: Science and Technology, 2024, 5 (3), pp.035053. ⟨10.1088/2632-2153/ad6ca0⟩. ⟨hal-04699130⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More