Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

Journal Article · 16 September 2021 · Physical Review Letters

DOI: https://doi.org/10.1103/physrevlett.127.120602 · OSTI ID:1829464

Casert, Corneel ^[1]; Vieijra, Tom ^[1]; Whitelam, Stephen ^[2]; Tamblyn, Isaac  ^[3]

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1. Ghent Univ. (Belgium)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Foundry
3. University of Ottawa, Ontario (Canada) Vector Institute for Artificial Intelligence, Ontario (Canada)

We use a neural-network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We use recurrent neural networks to describe the large deviations of the dynamical activity of model glasses, kinetically constrained models in two dimensions. We present the first finite size-scaling analysis of the large-deviation functions of the two-dimensional Fredrickson-Andersen model, and explore the spatial structure of the high-activity sector of the South-or-East model. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1829464
• Journal Information: Physical Review Letters, Journal Name: Physical Review Letters Journal Issue: 12 Vol. 127; ISSN 0031-9007
• Publisher: American Physical Society (APS)Copyright Statement
• Country of Publication: United States
• Language: English