• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Self-learning continuous-time Monte Carlo method in strongly correlated systems

Research Project

Project/Area Number 18K03552
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 13030:Magnetism, superconductivity and strongly correlated systems-related
Research InstitutionJapan Atomic Energy Agency

Principal Investigator

Nagai Yuki  国立研究開発法人日本原子力研究開発機構, システム計算科学センター, 副主任研究員 (20587026)

Project Period (FY) 2018-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Keywords自己学習モンテカルロ法 / 機械学習 / 強相関電子系 / モンテカルロ法 / 量子モンテカルロ
Outline of Final Research Achievements

We applied the self-learning Monte Carlo method to various kinds of fields, such as electron systems, molecular simulations, lattice quantum chromodynamics. For example, in the field of the molecular simulations, we developed self-learning hybrid Monte Carlo method, which is one of the best tools to generate very accurate neural network potentials.

Academic Significance and Societal Importance of the Research Achievements

自己学習モンテカルロ法を様々な分野へと適用できることを示した。特に原子分子シミュレーション分野における自己学習ハイブリッドモンテカルロ法は、第一原理分子動力学計算と呼ばれる材料物性分野において非常に重要なシミュレーションを大幅に高速化することが可能であり、学術的な重要性に加えて産業界への応用可能性も考えられる。

Report

(5 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (20 results)

All 2022 2021 2020 2019 2018

All Journal Article (8 results) (of which Peer Reviewed: 8 results,  Open Access: 2 results) Presentation (11 results) (of which Int'l Joint Research: 3 results,  Invited: 7 results) Book (1 results)

  • [Journal Article] Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica2021

    • Author(s)
      Kobayashi Keita、Nagai Yuki、Itakura Mitsuhiro、Shiga Motoyuki
    • Journal Title

      The Journal of Chemical Physics

      Volume: 155 Issue: 3 Pages: 034106-034106

    • DOI

      10.1063/5.0055341

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Effective Ruderman-Kittel-Kasuya-Yosida-like Interaction in Diluted Double-exchange Model: Self-learning Monte Carlo Approach2021

    • Author(s)
      Kohshiro Hidehiko、Nagai Yuki
    • Journal Title

      Journal of the Physical Society of Japan

      Volume: 90 Issue: 3 Pages: 034711-034711

    • DOI

      10.7566/jpsj.90.034711

    • NAID

      40022509772

    • Related Report
      2020 Research-status Report
    • Peer Reviewed
  • [Journal Article] Sparse modeling of large-scale quantum impurity models with low symmetries2021

    • Author(s)
      Shinaoka Hiroshi、Nagai Yuki
    • Journal Title

      Physical Review B

      Volume: 103 Issue: 4 Pages: 045120-1

    • DOI

      10.1103/physrevb.103.045120

    • Related Report
      2020 Research-status Report
    • Peer Reviewed
  • [Journal Article] Sparse modeling approach to obtaining the shear viscosity from smeared correlation functions2020

    • Author(s)
      Itou Etsuko、Nagai Yuki
    • Journal Title

      Journal of High Energy Physics

      Volume: 2020 Issue: 7 Pages: 1-31

    • DOI

      10.1007/jhep07(2020)007

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Self-learning hybrid Monte Carlo: A first-principles approach2020

    • Author(s)
      Nagai Yuki、Okumura Masahiko、Kobayashi Keita、Shiga Motoyuki
    • Journal Title

      Physical Review B

      Volume: 102 Issue: 4

    • DOI

      10.1103/physrevb.102.041124

    • Related Report
      2020 Research-status Report
    • Peer Reviewed
  • [Journal Article] Self-learning Monte Carlo method with Behler-Parrinello neural networks2020

    • Author(s)
      Nagai Yuki、Okumura Masahiko、Tanaka Akinori
    • Journal Title

      Physical Review B

      Volume: 101 Issue: 11 Pages: 115111-115111

    • DOI

      10.1103/physrevb.101.115111

    • NAID

      130008147942

    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Smooth Self-energy in the Exact-diagonalization-based Dynamical Mean-field Theory: Intermediate-representation Filtering Approach2019

    • Author(s)
      Nagai Yuki、Shinaoka Hiroshi
    • Journal Title

      Journal of the Physical Society of Japan

      Volume: 88 Issue: 6 Pages: 064004-064004

    • DOI

      10.7566/jpsj.88.064004

    • NAID

      40021915212

    • Related Report
      2019 Research-status Report
    • Peer Reviewed
  • [Journal Article] Smooth Self-energy in the Exact-diagonalization-based Dynamical Mean-field Theory: Intermediate-representation Filtering Approach2019

    • Author(s)
      Yuki Nagai and Hiroshi Shinaoka
    • Journal Title

      J. Phys. Soc. Jpn.

      Volume: 印刷中

    • NAID

      40021915212

    • Related Report
      2018 Research-status Report
    • Peer Reviewed
  • [Presentation] 自己学習ハイブリッドモンテカルロ法; 精度保障された機械学習分子シミュレーションと効率的な力場構築2022

    • Author(s)
      永井佑紀
    • Organizer
      令和3年度電気化学界面シミュレーションコンソーシアム第4回研究会
    • Related Report
      2021 Annual Research Report
    • Invited
  • [Presentation] 自己学習ハイブリッドモンテカルロ法; 精度保証された機械学習分子シミュレーション2021

    • Author(s)
      永井佑紀
    • Organizer
      レア・イベントの計算科学第4 回ワークショップ「レア・イベント解析とデータ科学」
    • Related Report
      2021 Annual Research Report
    • Invited
  • [Presentation] 精度保証された機械学習分子動力学法; 自己学習ハイブリッドモンテカルロ法2020

    • Author(s)
      永井佑紀
    • Organizer
      ディープラーニングと物理学 2020(第 1 回)
    • Related Report
      2020 Research-status Report
    • Invited
  • [Presentation] Self-learning Monte Carlo method; Speedup of the Markov chain Monte Carlo with machine learning2019

    • Author(s)
      Yuki Nagai
    • Organizer
      Quantum Engineering meets Harmonic Analysis, Saskatoon, Canada
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] 自己学習ハイブリッドモンテカルロ法:第一原理分子シミュレー ションの高速化2019

    • Author(s)
      永井佑紀, 奥村雅彦,小林恵太,志賀基之
    • Organizer
      日本物理学会2019年秋季大会
    • Related Report
      2019 Research-status Report
  • [Presentation] 自己学習ハイブリッドモンテカルロ法: 機械学習による第一原理分子シミュレーションの高速化2019

    • Author(s)
      永井佑紀, 奥村雅彦,小林恵太,志賀基之
    • Organizer
      2019分子シミュレーション討論会
    • Related Report
      2019 Research-status Report
  • [Presentation] Self-learning Hybrid Monte Carlo for first-principles molecular simulations2019

    • Author(s)
      Yuki Nagai
    • Organizer
      Deep learning and Physics 2019
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] Behler-Parrinello 型ニューラルネットワークを用いた自己学習モンテカルロ法2019

    • Author(s)
      永井佑紀、奥村雅彦、田中章詞
    • Organizer
      日本物理学会第 74 回年次大会
    • Related Report
      2018 Research-status Report
  • [Presentation] 自己学習モンテカルロ法; 機械学習を用いたモンテカルロ法の高速化2019

    • Author(s)
      永井佑紀
    • Organizer
      第 7 回材料系ワークショップ
    • Related Report
      2018 Research-status Report
    • Invited
  • [Presentation] 自己学習モンテカルロ法2018

    • Author(s)
      永井佑紀
    • Organizer
      深層学習と物理2018
    • Related Report
      2018 Research-status Report
    • Invited
  • [Presentation] Self-learning Monte Carlo method with neural networks inspired by machine-learning molecular dynamics2018

    • Author(s)
      永井佑紀、奥村雅彦、田中章詞
    • Organizer
      Mini-workshop on Machine Learning in Physics
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research / Invited
  • [Book] 物理学者,機械学習を使う2019

    • Author(s)
      橋本 幸士、大槻 東巳、真野 智裕、斎藤 弘樹、藤田 浩之、安藤 康伸、永井 佑紀、青木 健一、藤田 達大、小林 玉青、大関 真之、久良 尚任、福嶋 健二、村瀬 功一、船井 正太郎、柏 浩司、富谷 昭夫
    • Total Pages
      212
    • Publisher
      朝倉書店
    • ISBN
      4254131291
    • Related Report
      2019 Research-status Report

URL: 

Published: 2018-04-23   Modified: 2023-01-30  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi