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Speaker: Yuki Nagai (Japan Atomic Energy Agency)
Time & Date: 13:00 – 14:00, December 26, 2023 (JST)
Place: Hybrid (Room A614, ISSP / Zoom meeting)
Title: The Self-Learning Monte Carlo Method: Accelerating Simulations
with Machine Learning
Abstract:
We have introduced a general method, dubbed self-learning Monte Carlo
(SLMC), which speeds up the MC simulation by designing and training a
model to propose efficient global updates. We have developed the SLMC
in various kinds of systems for electrons[1], spins[2], atoms[3], and
quarks and gluons[4].
For example, we proposed an efficient approach called self-learning
hybrid Monte Carlo (SLHMC) method, which is a general method to make
use of machine learning (ML) potentials to accelerate the statistical
sampling of first principles density-functional-theory (DFT)
simulations[3]. In the SLHMC simulation, the statistical ensemble is
sampled exactly at the DFT level for a given thermodynamic condition.
Meanwhile, the ML potential is improved on the fly by training to
enhance the sampling, whereby the training dataset, which is sampled
from the exact ensemble, is created automatically.
In this talk, I will show the basic concept of SLMC and various kinds
of applications.
[1] YN, H. Shen, Y. Qi, J. Liu, and L. Fu, Self-Learning Monte Carlo
Method: Continuous-Time Algorithm, Phys. Rev. B 96, 161102 (2017).;
YN, M. Okumura, K. Kobayashi, and M. Shiga, Self-Learning Hybrid Monte
Carlo: A First-Principles Approach, Phys. Rev. B 102, 041124 (2020).
[2] H. Kohshiro and YN, Effective Ruderman–Kittel–Kasuya–Yosida-like
Interaction in Diluted Double-Exchange Model: Self-Learning Monte
Carlo Approach, J. Phys. Soc. Jpn. 90, 034711 (2021).;YN and A.
Tomiya, Self-Learning Monte Carlo with Equivariant Transformer,
arxiv.org/abs/2306.11527.
[3] YN, M. Okumura, K. Kobayashi, and M. Shiga, Self-Learning Hybrid
Monte Carlo: A First-Principles Approach, Phys. Rev. B 102, 041124
(2020).;K. Kobayashi, YN, M. Itakura, and M. Shiga, Self-Learning
Hybrid Monte Carlo Method for Isothermal-Isobaric Ensemble:
Application to Liquid Silica, J. Chem. Phys. 155, 034106 (2021).
[4] YN, A. Tanaka, and A. Tomiya, Self-Learning Monte Carlo for
Non-Abelian Gauge Theory with Dynamical Fermions, Phys. Rev. D
(2023).;Y. Nagai and A. Tomiya, Gauge Covariant Neural Network for 4
Dimensional Non-Abelian Gauge Theory, arxiv.org/abs/2103.11965.
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—
Satoshi Morita (森田 悟史)
Faculty of Science and Technology, Keio University
smorita.github.io/
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