Self-learning Monte Carlo with deep neural networks

doi:10.1103/PhysRevB.97.205140

Title: Self-learning Monte Carlo with deep neural networks

Authors:: Shen, Huitao; Liu, Junwei; Fu, Liang

Publication Date:: 2018-05-29

Sponsoring Org.:: USDOE

OSTI Identifier:: 1439375

Grant/Contract Number:: SC0010526

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Physical Review B

Additional Journal Information:: Journal Name: Physical Review B Journal Volume: 97 Journal Issue: 20; Journal ID: ISSN 2469-9950

Publisher:: American Physical Society

Country of Publication:: United States

Language:: English

Citation Formats


                    Shen, Huitao, Liu, Junwei, and Fu, Liang. Self-learning Monte Carlo with deep neural networks.  United States: N. p., 2018. 
        Web.  doi:10.1103/PhysRevB.97.205140.

Copy to clipboard


                    Shen, Huitao, Liu, Junwei, & Fu, Liang. Self-learning Monte Carlo with deep neural networks.  United States.  doi:10.1103/PhysRevB.97.205140.

Copy to clipboard


                    Shen, Huitao, Liu, Junwei, and Fu, Liang. Tue .  
        "Self-learning Monte Carlo with deep neural networks".  United States.  doi:10.1103/PhysRevB.97.205140.

Copy to clipboard


                    
@article{osti_1439375,

  title        = {Self-learning Monte Carlo with deep neural networks},

  author       = {Shen, Huitao and Liu, Junwei and Fu, Liang},

  abstractNote = {},

  doi          = {10.1103/PhysRevB.97.205140},

  journal      = {Physical Review B},

  number       = 20,

  volume       = 97,

  place        = {United States},

  year         = {2018},

  month        = {5}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
DOI: 10.1103/PhysRevB.97.205140

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 9 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Quantum Entanglement in Neural Network States
journal, May 2017

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.
Physical Review X, Vol. 7, Issue 2
DOI: 10.1103/PhysRevX.7.021021

Monte Carlo Method for Magnetic Impurities in Metals
journal, June 1986

Hirsch, J. E.; Fye, R. M.
Physical Review Letters, Vol. 56, Issue 23
DOI: 10.1103/PhysRevLett.56.2521

Machine learning for many-body physics: The case of the Anderson impurity model
journal, October 2014

Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole
Physical Review B, Vol. 90, Issue 15
DOI: 10.1103/PhysRevB.90.155136

Self-learning Monte Carlo method
journal, January 2017

Liu, Junwei; Qi, Yang; Meng, Zi Yang
Physical Review B, Vol. 95, Issue 4
DOI: 10.1103/PhysRevB.95.041101

Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders
journal, August 2017

Wetzel, Sebastian J.
Physical Review E, Vol. 96, Issue 2
DOI: 10.1103/PhysRevE.96.022140

Equivalence of restricted Boltzmann machines and tensor network states
journal, February 2018

Chen, Jing; Cheng, Song; Xie, Haidong
Physical Review B, Vol. 97, Issue 8
DOI: 10.1103/PhysRevB.97.085104

Accelerated Monte Carlo simulations with restricted Boltzmann machines
journal, January 2017

Huang, Li; Wang, Lei
Physical Review B, Vol. 95, Issue 3
DOI: 10.1103/PhysRevB.95.035105

Self-learning Monte Carlo method: Continuous-time algorithm
journal, October 2017

Nagai, Yuki; Shen, Huitao; Qi, Yang
Physical Review B, Vol. 96, Issue 16
DOI: 10.1103/PhysRevB.96.161102

Machine Learning Topological Invariants with Neural Networks
journal, February 2018

Zhang, Pengfei; Shen, Huitao; Zhai, Hui
Physical Review Letters, Vol. 120, Issue 6
DOI: 10.1103/PhysRevLett.120.066401

Unifying neural-network quantum states and correlator product states via tensor networks
journal, February 2018

Clark, Stephen R.
Journal of Physics A: Mathematical and Theoretical, Vol. 51, Issue 13
DOI: 10.1088/1751-8121/aaaaf2

Efficient representation of quantum many-body states with deep neural networks
journal, September 2017

Gao, Xun; Duan, Lu-Ming
Nature Communications, Vol. 8, Issue 1
DOI: 10.1038/s41467-017-00705-2

Approximation capabilities of multilayer feedforward networks
journal, January 1991

Hornik, Kurt
Neural Networks, Vol. 4, Issue 2
DOI: 10.1016/0893-6080(91)90009-T

Localized Magnetic States in Metals
journal, October 1961

Anderson, P. W.
Physical Review, Vol. 124, Issue 1
DOI: 10.1103/PhysRev.124.41

Scaling Theory of the Asymmetric Anderson Model
journal, February 1978

Haldane, F. D. M.
Physical Review Letters, Vol. 40, Issue 6
DOI: 10.1103/PhysRevLett.40.416

Numerical study of the two-dimensional Hubbard model
journal, July 1989

White, S. R.; Scalapino, D. J.; Sugar, R. L.
Physical Review B, Vol. 40, Issue 1
DOI: 10.1103/PhysRevB.40.506

Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice
journal, January 2018

Saito, Hiroki; Kato, Masaya
Journal of the Physical Society of Japan, Vol. 87, Issue 1
DOI: 10.7566/JPSJ.87.014001

Learning disordered topological phases by statistical recovery of symmetry
journal, May 2018

Yoshioka, Nobuyuki; Akagi, Yutaka; Katsura, Hosho
Physical Review B, Vol. 97, Issue 20
DOI: 10.1103/PhysRevB.97.205110

Monte Carlo study of the symmetric Anderson-impurity model
journal, July 1988

Fye, R. M.; Hirsch, J. E.
Physical Review B, Vol. 38, Issue 1
DOI: 10.1103/PhysRevB.38.433

Continuous-time Monte Carlo methods for quantum impurity models
journal, May 2011

Gull, Emanuel; Millis, Andrew J.; Lichtenstein, Alexander I.
Reviews of Modern Physics, Vol. 83, Issue 2
DOI: 10.1103/RevModPhys.83.349

Quantum Monte Carlo with directed loops
journal, October 2002

Syljuåsen, Olav F.; Sandvik, Anders W.
Physical Review E, Vol. 66, Issue 4
DOI: 10.1103/PhysRevE.66.046701

Approximation by superpositions of a sigmoidal function
journal, December 1989

Cybenko, G.
Mathematics of Control, Signals, and Systems, Vol. 2, Issue 4
DOI: 10.1007/BF02551274

Neural-Network Quantum States, String-Bond States, and Chiral Topological States
journal, January 2018

Glasser, Ivan; Pancotti, Nicola; August, Moritz
Physical Review X, Vol. 8, Issue 1
DOI: 10.1103/PhysRevX.8.011006

Probing many-body localization with neural networks
journal, June 2017

Schindler, Frank; Regnault, Nicolas; Neupert, Titus
Physical Review B, Vol. 95, Issue 24
DOI: 10.1103/PhysRevB.95.245134

Restricted Boltzmann machine learning for solving strongly correlated quantum systems
journal, November 2017

Nomura, Yusuke; Darmawan, Andrew S.; Yamaji, Youhei
Physical Review B, Vol. 96, Issue 20
DOI: 10.1103/PhysRevB.96.205152

Self-learning quantum Monte Carlo method in interacting fermion systems
journal, July 2017

Xu, Xiao Yan; Qi, Yang; Liu, Junwei
Physical Review B, Vol. 96, Issue 4
DOI: 10.1103/PhysRevB.96.041119

Machine learning of frustrated classical spin models. I. Principal component analysis
journal, October 2017

Wang, Ce; Zhai, Hui
Physical Review B, Vol. 96, Issue 14
DOI: 10.1103/PhysRevB.96.144432

Recommender engine for continuous-time quantum Monte Carlo methods
journal, March 2017

Huang, Li; Yang, Yi-feng; Wang, Lei
Physical Review E, Vol. 95, Issue 3
DOI: 10.1103/PhysRevE.95.031301

Discovering phase transitions with unsupervised learning
journal, November 2016

Wang, Lei
Physical Review B, Vol. 94, Issue 19
DOI: 10.1103/PhysRevB.94.195105

Deep learning and the Schrödinger equation
journal, October 2017

Mills, Kyle; Spanner, Michael; Tamblyn, Isaac
Physical Review A, Vol. 96, Issue 4
DOI: 10.1103/PhysRevA.96.042113

Performance analysis of continuous-time solvers for quantum impurity models
journal, December 2007

Gull, Emanuel; Werner, Philipp; Millis, Andrew
Physical Review B, Vol. 76, Issue 23
DOI: 10.1103/PhysRevB.76.235123

Identifying product order with restricted Boltzmann machines
journal, March 2018

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong
Physical Review B, Vol. 97, Issue 9
DOI: 10.1103/PhysRevB.97.094207

Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions
journal, January 1996

Georges, Antoine; Kotliar, Gabriel; Krauth, Werner
Reviews of Modern Physics, Vol. 68, Issue 1
DOI: 10.1103/RevModPhys.68.13

Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
journal, December 2016

Ohtsuki, Tomoki; Ohtsuki, Tomi
Journal of the Physical Society of Japan, Vol. 85, Issue 12
DOI: 10.7566/JPSJ.85.123706

Two-dimensional Hubbard model: Numerical simulation study
journal, April 1985

Hirsch, J. E.
Physical Review B, Vol. 31, Issue 7
DOI: 10.1103/PhysRevB.31.4403

Monte Carlo calculations of coupled boson-fermion systems. I
journal, October 1981

Blankenbecler, R.; Scalapino, D. J.; Sugar, R. L.
Physical Review D, Vol. 24, Issue 8
DOI: 10.1103/PhysRevD.24.2278

Computational Methods in Coupled Electron-Ion Monte Carlo Simulations
journal, September 2005

Pierleoni, Carlo; Ceperley, David M.
ChemPhysChem, Vol. 6, Issue 9
DOI: 10.1002/cphc.200400587

Identifying quantum phase transitions with adversarial neural networks
journal, April 2018

Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter
Physical Review B, Vol. 97, Issue 13
DOI: 10.1103/PhysRevB.97.134109

Self-learning Monte Carlo method and cumulative update in fermion systems
journal, June 2017

Liu, Junwei; Shen, Huitao; Qi, Yang
Physical Review B, Vol. 95, Issue 24
DOI: 10.1103/PhysRevB.95.241104

Quantum Loop Topography for Machine Learning
journal, May 2017

Zhang, Yi; Kim, Eun-Ah
Physical Review Letters, Vol. 118, Issue 21
DOI: 10.1103/PhysRevLett.118.216401

Detection of Phase Transition via Convolutional Neural Networks
journal, June 2017

Tanaka, Akinori; Tomiya, Akio
Journal of the Physical Society of Japan, Vol. 86, Issue 6
DOI: 10.7566/JPSJ.86.063001

Reptation quantum Monte Carlo algorithm for lattice Hamiltonians with a directed-update scheme
journal, October 2010

Carleo, Giuseppe; Becca, Federico; Moroni, Saverio
Physical Review E, Vol. 82, Issue 4
DOI: 10.1103/PhysRevE.82.046710

Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination
journal, June 2017

Hu, Wenjian; Singh, Rajiv R. P.; Scalettar, Richard T.
Physical Review E, Vol. 95, Issue 6
DOI: 10.1103/PhysRevE.95.062122

Directed update for the stochastic Green function algorithm
journal, November 2008

Rousseau, V. G.
Physical Review E, Vol. 78, Issue 5
DOI: 10.1103/PhysRevE.78.056707

Learning phase transitions by confusion
journal, February 2017

van Nieuwenburg, Evert P. L.; Liu, Ye-Hua; Huber, Sebastian D.
Nature Physics, Vol. 13, Issue 5
DOI: 10.1038/nphys4037

Solving the quantum many-body problem with artificial neural networks
journal, February 2017

Carleo, Giuseppe; Troyer, Matthias
Science, Vol. 355, Issue 6325
DOI: 10.1126/science.aag2302

Machine learning phases of matter
journal, February 2017

Carrasquilla, Juan; Melko, Roger G.
Nature Physics, Vol. 13, Issue 5
DOI: 10.1038/nphys4035

Neural-network quantum state tomography
journal, February 2018

Torlai, Giacomo; Mazzola, Guglielmo; Carrasquilla, Juan
Nature Physics, Vol. 14, Issue 5
DOI: 10.1038/s41567-018-0048-5

Similar records in OSTI.GOV collections:

Deep learning for computational chemistry

Journal Article Goh, Garrett B. ; Hodas, Nathan O. ; Vishnu, Abhinav - Journal of Computational Chemistry

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore »« less
DOI: 10.1002/jcc.24764
Integrate Light-Weight Deep Learning Tools with Internet of Things

Technical Report Sun, Yu ; Yu, Dantong

Today’s smart devices, such as a collision avoidance system, rely on detection of past and current eventsand then react to mitigate and reduce the severity of the ultimate consequence. The lack of proactivenessoften renders such a detection system less useful for an emergency. The ability to predict the future basedon what has been heard, sensed, observed at the current moment is of utmost importance for real-timesystems, such as autonomous driving, safety, and security monitoring and warning. It is quite commonthat a prediction system completely reconstructs (i.e., predicts) the raw image pixels of the future frame.However it does not necessarily reflectmore »« less
Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

Technical Report Yoo, S.J.Ben

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: •Designed a network architecture for automated multi-domain service provisioning: oDesigned information exchange schemes between autonomous systems (domains) oDesigned application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networksbelonging to different autonomous systems oExperimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents •Designed a broker-based orchestratedmore »« less
DOI: 10.2172/1579715

Full Text Available
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Journal Article Jin, Baihong ; Li, Dan ; Srinivasan, Seshadhri ; ...

Early detection of incipient faults is of significant importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To address this concern, this papermore »« less
DOI: 10.1109/icphm.2019.8819438
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

Journal Article Perreault Levasseur, Laurence ; Hezaveh, Yashar D. ; Wechsler, Risa H. - The Astrophysical Journal. Letters

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more »« less
Cited by 3
DOI: 10.3847/2041-8213/aa9704

Full Text Available

Similar Records

Title: Self-learning Monte Carlo with deep neural networks

Citation Formats

Quantum Entanglement in Neural Network States journal, May 2017

Monte Carlo Method for Magnetic Impurities in Metals journal, June 1986

Machine learning for many-body physics: The case of the Anderson impurity model journal, October 2014

Self-learning Monte Carlo method journal, January 2017

Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders journal, August 2017

Equivalence of restricted Boltzmann machines and tensor network states journal, February 2018

Accelerated Monte Carlo simulations with restricted Boltzmann machines journal, January 2017

Self-learning Monte Carlo method: Continuous-time algorithm journal, October 2017

Machine Learning Topological Invariants with Neural Networks journal, February 2018

Unifying neural-network quantum states and correlator product states via tensor networks journal, February 2018

Efficient representation of quantum many-body states with deep neural networks journal, September 2017

Approximation capabilities of multilayer feedforward networks journal, January 1991

Localized Magnetic States in Metals journal, October 1961

Scaling Theory of the Asymmetric Anderson Model journal, February 1978

Numerical study of the two-dimensional Hubbard model journal, July 1989

Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice journal, January 2018

Learning disordered topological phases by statistical recovery of symmetry journal, May 2018

Monte Carlo study of the symmetric Anderson-impurity model journal, July 1988

Continuous-time Monte Carlo methods for quantum impurity models journal, May 2011

Quantum Monte Carlo with directed loops journal, October 2002

Approximation by superpositions of a sigmoidal function journal, December 1989

Neural-Network Quantum States, String-Bond States, and Chiral Topological States journal, January 2018

Probing many-body localization with neural networks journal, June 2017

Restricted Boltzmann machine learning for solving strongly correlated quantum systems journal, November 2017

Self-learning quantum Monte Carlo method in interacting fermion systems journal, July 2017

Machine learning of frustrated classical spin models. I. Principal component analysis journal, October 2017

Recommender engine for continuous-time quantum Monte Carlo methods journal, March 2017

Discovering phase transitions with unsupervised learning journal, November 2016

Deep learning and the Schrödinger equation journal, October 2017

Performance analysis of continuous-time solvers for quantum impurity models journal, December 2007

Identifying product order with restricted Boltzmann machines journal, March 2018

Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions journal, January 1996

Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems journal, December 2016

Two-dimensional Hubbard model: Numerical simulation study journal, April 1985

Monte Carlo calculations of coupled boson-fermion systems. I journal, October 1981

Computational Methods in Coupled Electron-Ion Monte Carlo Simulations journal, September 2005

Identifying quantum phase transitions with adversarial neural networks journal, April 2018

Self-learning Monte Carlo method and cumulative update in fermion systems journal, June 2017

Quantum Loop Topography for Machine Learning journal, May 2017

Detection of Phase Transition via Convolutional Neural Networks journal, June 2017

Reptation quantum Monte Carlo algorithm for lattice Hamiltonians with a directed-update scheme journal, October 2010

Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination journal, June 2017

Directed update for the stochastic Green function algorithm journal, November 2008

Learning phase transitions by confusion journal, February 2017

Solving the quantum many-body problem with artificial neural networks journal, February 2017

Machine learning phases of matter journal, February 2017

Neural-network quantum state tomography journal, February 2018

Quantum Entanglement in Neural Network States
journal, May 2017

Monte Carlo Method for Magnetic Impurities in Metals
journal, June 1986

Machine learning for many-body physics: The case of the Anderson impurity model
journal, October 2014

Self-learning Monte Carlo method
journal, January 2017

Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders
journal, August 2017

Equivalence of restricted Boltzmann machines and tensor network states
journal, February 2018

Accelerated Monte Carlo simulations with restricted Boltzmann machines
journal, January 2017

Self-learning Monte Carlo method: Continuous-time algorithm
journal, October 2017

Machine Learning Topological Invariants with Neural Networks
journal, February 2018

Unifying neural-network quantum states and correlator product states via tensor networks
journal, February 2018

Efficient representation of quantum many-body states with deep neural networks
journal, September 2017

Approximation capabilities of multilayer feedforward networks
journal, January 1991

Localized Magnetic States in Metals
journal, October 1961

Scaling Theory of the Asymmetric Anderson Model
journal, February 1978

Numerical study of the two-dimensional Hubbard model
journal, July 1989

Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice
journal, January 2018

Learning disordered topological phases by statistical recovery of symmetry
journal, May 2018

Monte Carlo study of the symmetric Anderson-impurity model
journal, July 1988

Continuous-time Monte Carlo methods for quantum impurity models
journal, May 2011

Quantum Monte Carlo with directed loops
journal, October 2002

Approximation by superpositions of a sigmoidal function
journal, December 1989

Neural-Network Quantum States, String-Bond States, and Chiral Topological States
journal, January 2018

Probing many-body localization with neural networks
journal, June 2017

Restricted Boltzmann machine learning for solving strongly correlated quantum systems
journal, November 2017

Self-learning quantum Monte Carlo method in interacting fermion systems
journal, July 2017

Machine learning of frustrated classical spin models. I. Principal component analysis
journal, October 2017

Recommender engine for continuous-time quantum Monte Carlo methods
journal, March 2017

Discovering phase transitions with unsupervised learning
journal, November 2016

Deep learning and the Schrödinger equation
journal, October 2017

Performance analysis of continuous-time solvers for quantum impurity models
journal, December 2007

Identifying product order with restricted Boltzmann machines
journal, March 2018

Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions
journal, January 1996

Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
journal, December 2016

Two-dimensional Hubbard model: Numerical simulation study
journal, April 1985

Monte Carlo calculations of coupled boson-fermion systems. I
journal, October 1981

Computational Methods in Coupled Electron-Ion Monte Carlo Simulations
journal, September 2005

Identifying quantum phase transitions with adversarial neural networks
journal, April 2018

Self-learning Monte Carlo method and cumulative update in fermion systems
journal, June 2017

Quantum Loop Topography for Machine Learning
journal, May 2017

Detection of Phase Transition via Convolutional Neural Networks
journal, June 2017

Reptation quantum Monte Carlo algorithm for lattice Hamiltonians with a directed-update scheme
journal, October 2010

Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination
journal, June 2017

Directed update for the stochastic Green function algorithm
journal, November 2008

Learning phase transitions by confusion
journal, February 2017

Solving the quantum many-body problem with artificial neural networks
journal, February 2017

Machine learning phases of matter
journal, February 2017

Neural-network quantum state tomography
journal, February 2018