学术, 文献整理

常见的凝聚态物理和机器学习文献

这里记录一些常见的凝聚态物理和机器学习文献。

由于偏第一性原理计算、分子、材料、化学、实验等方向的文献引用率相对比较高,这里对这类文献进行单独记录和更多过滤,与相变、拓扑、多体等方向的文献做了大致的区分。

“文献整理”系列的通用前置说明:以下主要参考综述文献、文章的引言,以及使用搜索引擎和文献关联工具等。内容以早期的引用率高的文献为主,按年份时间进行排列,同年份的不分先后。列表大概率不完整,存在遗漏,仅供参考,可能不定期补充更新。如果需要阅读更多的或者最新的文献,可自行搜索查找。

一、论文(偏相变、拓扑、多体等方向)

  1. 2016 - Wang - Discovering phase transitions with unsupervised learning https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.195105
  2. 2016 - Torlai and Melko - Learning thermodynamics with Boltzmann machines https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.165134
  3. 2016 - Ohtsuki and Ohtsuki - Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems https://journals.jps.jp/doi/full/10.7566/JPSJ.85.123706
  4. 2017 - Carleo and Troyer - Solving the quantum many-body problem with artificial neural networks https://www.science.org/doi/10.1126/science.aag2302
  5. 2017 - Carrasquilla and Melko - Machine learning phases of matter https://www.nature.com/articles/nphys4035
  6. 2017 - van Nieuwenburg et al. - Learning phase transitions by confusion https://www.nature.com/articles/nphys4037
  7. 2017 - Deng et al. - Quantum Entanglement in Neural Network States https://journals.aps.org/prx/abstract/10.1103/PhysRevX.7.021021
  8. 2017 - Liu et al. - Self-learning Monte Carlo method https://journals.aps.org/prb/abstract/10.1103/PhysRevB.95.041101
  9. 2017 - Gao and Duan - Efficient representation of quantum many-body states with deep neural networks https://www.nature.com/articles/s41467-017-00705-2
  10. 2017 - Broecker et al. - Machine learning quantum phases of matter beyond the fermion sign problem https://www.nature.com/articles/s41598-017-09098-0
  11. 2017 - Ch'ng et al. - Machine Learning Phases of Strongly Correlated Fermions https://journals.aps.org/prx/abstract/10.1103/PhysRevX.7.031038
  12. 2017 - Zhang and Kim - Quantum Loop Topography for Machine Learning https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.216401
  13. 2017 - Deng et al. - Machine learning topological states https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.195145
  14. 2017 - Hu et al. - Discovering phases, phase transitions, and crossovers through unsupervised machine learning A critical examination https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.062122
  15. 2017 - Huang and Wang - Accelerated Monte Carlo simulations with restricted Boltzmann machines https://journals.aps.org/prb/abstract/10.1103/PhysRevB.95.035105
  16. 2017 - Nomura et al. - Restricted Boltzmann machine learning for solving strongly correlated quantum systems https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.205152
  17. 2017 - Tanaka and Tomiya - Detection of Phase Transition via Convolutional Neural Networks https://journals.jps.jp/doi/10.7566/JPSJ.86.063001
  18. 2017 - Schindler et al. - Probing many-body localization with neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.95.245134
  19. 2017 - Ohtsuki and Ohtsuki - Deep Learning the Quantum Phase Transitions in Random Electron Systems Applications to Three Dimensions https://journals.jps.jp/doi/10.7566/JPSJ.86.044708
  20. 2017 - Zhang et al. - Machine learning Z2 quantum spin liquids with quasiparticle statistics https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.245119
  21. 2017 - Ponte and Melko - Kernel methods for interpretable machine learning of order parameters https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.205146
  22. 2017 - Wang and Zhai - Machine learning of frustrated classical spin models. I. Principal component analysis https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.144432
  23. 2017 - Nagai et al. - Self-learning Monte Carlo method Continuous-time algorithm https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.161102
  24. 2017 - Wetzel - Unsupervised learning of phase transitions From principal component analysis to variational autoencoders https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.022140
  25. 2017 - Wetzel and Scherzer - Machine learning of explicit order parameters From the Ising model to SU(2) lattice gauge theory https://journals.aps.org/prb/abstract/10.1103/PhysRevB.96.184410
  26. 2017 - Saito - Solving the Bose–Hubbard Model with Machine Learning https://journals.jps.jp/doi/10.7566/JPSJ.86.093001
  27. 2018 - Torlai et al. - Neural-network quantum state tomography https://www.nature.com/articles/s41567-018-0048-5
  28. 2018 - Chen et al. - Equivalence of restricted Boltzmann machines and tensor network states https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.085104
  29. 2018 - Zhang et al. - Machine Learning Topological Invariants with Neural Networks https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.066401
  30. 2018 - Sun - Deep learning topological invariants of band insulators https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.085402
  31. 2018 - Venderley et al. - Machine Learning Out-of-Equilibrium Phases of Matter https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.257204
  32. 2018 - Beach - Machine learning vortices at the Kosterlitz-Thouless transition https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.045207
  33. 2018 - Ch'ng et al. - Unsupervised machine learning account of magnetic transitions in the Hubbard model https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.013306
  34. 2018 - Huembeli et al. - Identifying quantum phase transitions with adversarial neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.134109
  35. 2018 - Glasser et al. - Neural-Network Quantum States, String-Bond States, and Chiral Topological States https://journals.aps.org/prx/abstract/10.1103/PhysRevX.8.011006
  36. 2018 - Cai and Liu - Approximating quantum many-body wave functions using artificial neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.035116
  37. 2018 - Carleo et al. - Constructing exact representations of quantum many-body systems with deep neural networks https://www.nature.com/articles/s41467-018-07520-3
  38. 2018 - Choo et al. - Symmetries and Many-Body Excitations with Neural-Network Quantum States https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.121.167204
  39. 2018 - Liu and van Nieuwenburg - Discriminative Cooperative Networks for Detecting Phase Transitions https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.176401
  40. 2018 - Suchsland and Wessel - Parameter diagnostics of phases and phase transition learning by neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.174435
  41. 2018 - Shen et al. - Self-learning Monte Carlo with deep neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.205140
  42. 2018 - Yoshioka et al. - Learning disordered topological phases by statistical recovery of symmetry https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.205110
  43. 2018 - Wang and Zhai - Machine learning of frustrated classical spin models (II) Kernel principal component analysis https://link.springer.com/article/10.1007/s11467-018-0798-7
  44. 2018 - Kim and Kim - Smallest neural network to learn the Ising criticality https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.022138
  45. 2018 - Vargas-Hernández et al. - Extrapolating Quantum Observables with Machine Learning Inferring Multiple Phase Transitions from Properties of a Single Phase https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.121.255702
  46. 2018 - Torlai and Melko - Latent Space Purification via Neural Density Operators https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.240503
  47. 2018 - Saito and Kato - Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice https://journals.jps.jp/doi/full/10.7566/JPSJ.87.014001?mobileUi=0
  48. 2018 - Kaubruegger et al. - Chiral topological phases from artificial neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.195136
  49. 2018 - Deng - Machine Learning Detection of Bell Nonlocality in Quantum Many-Body Systems https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.240402
  50. 2018 - Clark - Unifying neural-network quantum states and correlator product states via tensor networks https://iopscience.iop.org/article/10.1088/1751-8121/aaaaf2
  51. 2018 - Liang et al. - Solving frustrated quantum many-particle models with convolutional neural networks https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.104426
  52. 2019 - Rodriguez-Nieva and Scheurer - Identifying topological order through unsupervised machine learning https://www.nature.com/articles/s41567-019-0512-x
  53. 2019 - Dong et al. - Machine learning of quantum phase transitions https://journals.aps.org/prb/abstract/10.1103/PhysRevB.99.121104
  54. 2019 - Giannetti et al. - Machine Learning as a universal tool for quantitative investigations of phase transitions https://www.sciencedirect.com/science/article/pii/S0550321319301257
  55. 2019 - Kashiwa et al. - Phase transition encoded in neural network https://academic.oup.com/ptep/article/2019/8/083A04/5553575
  56. 2019 - Carrasquilla et al. - Reconstructing quantum states with generative models https://www.nature.com/articles/s42256-019-0028-1
  57. 2019 - Levine et al. - Quantum Entanglement in Deep Learning Architectures https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.065301
  58. 2019 - Melko et al. - Restricted Boltzmann machines in quantum physics https://www.nature.com/articles/s41567-019-0545-1
  59. 2019 - Rem et al. - Identifying quantum phase transitions using artificial neural networks on experimental data https://www.nature.com/articles/s41567-019-0554-0
  60. 2019 - Choo et al. - Two-dimensional frustrated J1−J2 model studied with neural network quantum states https://journals.aps.org/prb/abstract/10.1103/PhysRevB.100.125124
  61. 2019 - Luo and Clark - Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.226401
  62. 2019 - Torlai et al. - Integrating Neural Networks with a Quantum Simulator for State Reconstruction https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.123.230504
  63. 2019 - Lian et al. - Machine Learning Topological Phases with a Solid-State Quantum Simulator https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.210503
  64. 2019 - Zhang et al. - Machine learning of phase transitions in the percolation and XY models https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.032142
  65. 2019 - Yoshioka and Hamazaki - Constructing neural stationary states for open quantum many-body systems https://journals.aps.org/prb/abstract/10.1103/PhysRevB.99.214306
  66. 2019 - Kalantre et al. - Machine learning techniques for state recognition and auto-tuning in quantum dots https://www.nature.com/articles/s41534-018-0118-7
  67. 2019 - Bohrdt et al. - Classifying snapshots of the doped Hubbard model with machine learning https://www.nature.com/articles/s41567-019-0565-x
  68. 2020 - Scheurer and Slager - Unsupervised Machine Learning and Band Topology https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.226401
  69. 2020 - Che et al. - Topological quantum phase transitions retrieved through unsupervised machine learning https://journals.aps.org/prb/abstract/10.1103/PhysRevB.102.134213
  70. 2020 - Zhang et al. - Interpreting machine learning of topological quantum phase transitions https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023283
  71. 2020 - Wetzel et al. - Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033499
  72. 2020 - Westerhout et al. - Generalization properties of neural network approximations to frustrated magnet ground states https://www.nature.com/articles/s41467-020-15402-w
  73. 2020 - Szabó and Castelnovo - Neural network wave functions and the sign problem https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033075
  74. 2020 - Hibat-Allah et al. - Recurrent neural network wave functions https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023358
  75. 2020 - Vieijra et al. - Restricted Boltzmann Machines for Quantum States with Non-Abelian or Anyonic Symmetries https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.097201
  76. 2021 - Nomura - Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry https://iopscience.iop.org/article/10.1088/1361-648X/abe268/meta
  77. 2022 - Vicentini et al. - NetKet 3 Machine Learning Toolbox for Many-Body Quantum Systems https://scipost.org/10.21468/SciPostPhysCodeb.7
  78. ……

二、论文(偏第一性原理计算、分子、材料、化学、实验等方向)

  1. 2007 - Behler and Parrinello - Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.146401
  2. 2011 - Behler - Atom-centered symmetry functions for constructing high-dimensional neural network potentials https://pubs.aip.org/aip/jcp/article-abstract/134/7/074106/954787/
  3. 2011 - Behler - Neural network potential-energy surfaces in chemistry a tool for large-scale simulations https://pubs.rsc.org/en/content/articlelanding/2011/cp/c1cp21668f
  4. 2012 - Rupp et al. - Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.058301
  5. 2012 - Snyder et al. - Finding Density Functionals with Machine Learning https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.253002
  6. 2013 - Montavon et al. - Machine learning of molecular electronic properties in chemical compound space https://iopscience.iop.org/article/10.1088/1367-2630/15/9/095003
  7. 2013 - Bartók et al. - On representing chemical environments https://journals.aps.org/prb/abstract/10.1103/PhysRevB.87.184115
  8. 2013 - Hansen et al. - Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies https://pubs.acs.org/doi/10.1021/ct400195d
  9. 2013 - Pilania et al. - Accelerating materials property predictions using machine learning https://www.nature.com/articles/srep02810
  10. 2013 - Saal et al. - Materials Design and Discovery with High-Throughput Density Functional Theory The Open Quantum Materials Database (OQMD) https://www.osti.gov/biblio/1383293
  11. 2014 - Ramakrishnan et al. - Quantum chemistry structures and properties of 134 kilo molecules https://www.nature.com/articles/sdata201422
  12. 2015 - Hansen et al. - Machine Learning Predictions of Molecular Properties Accurate Many-Body Potentials and Nonlocality in Chemical Space https://pubs.acs.org/doi/10.1021/acs.jpclett.5b00831
  13. 2015 - Ghiringhelli et al. - Big Data of Materials Science Critical Role of the Descriptor https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.105503
  14. 2015 - Thompson et al. - Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials https://www.sciencedirect.com/science/article/abs/pii/S0021999114008353
  15. 2015 - Kirklin et al. - The Open Quantum Materials Database (OQMD) assessing the accuracy of DFT formation energies https://www.nature.com/articles/npjcompumats201510
  16. 2016 - Kearnes et al. - Molecular graph convolutions moving beyond fingerprints https://link.springer.com/article/10.1007/s10822-016-9938-8
  17. 2016 - Ward et al. - A general-purpose machine learning framework for predicting properties of inorganic materials https://www.nature.com/articles/npjcompumats201628
  18. 2016 - Shapeev - Moment Tensor Potentials A Class of Systematically Improvable Interatomic Potentials https://epubs.siam.org/doi/10.1137/15M1054183
  19. 2016 - Raccuglia et al. - Machine-learning-assisted materials discovery using failed experiments https://www.nature.com/articles/nature17439
  20. 2017 - Schütt et al. - Quantum-chemical insights from deep tensor neural networks https://www.nature.com/articles/ncomms13890
  21. 2017 - Smith et al. - ANI-1 an extensible neural network potential with DFT accuracy at force field computational cost https://pubs.rsc.org/en/content/articlelanding/2017/sc/c6sc05720a
  22. 2017 - Chmiela et al. - Machine learning of accurate energy-conserving molecular force fields https://www.science.org/doi/10.1126/sciadv.1603015
  23. 2017 - Schütt et al. - SchNet A continuous-filter convolutional neural network for modeling quantum interactions https://dl.acm.org/doi/abs/10.5555/3294771.3294866
  24. 2017 - Brockherde et al. - Bypassing the Kohn-Sham equations with machine learning https://www.nature.com/articles/s41467-017-00839-3
  25. 2017 - Bartók et al. - Machine learning unifies the modeling of materials and molecules https://www.science.org/doi/10.1126/sciadv.1701816
  26. 2017 - Liu et al. - Materials discovery and design using machine learning https://www.sciencedirect.com/science/article/pii/S2352847817300515
  27. 2018 - Schütt et al. - SchNet – A deep learning architecture for molecules and materials https://pubs.aip.org/aip/jcp/article-abstract/148/24/241722/962591/
  28. 2018 - Chmiela et al. - Towards exact molecular dynamics simulations with machine-learned force fields https://www.nature.com/articles/s41467-018-06169-2
  29. 2018 - Xie and Grossman - Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301
  30. 2018 - Zhang et al. - Deep Potential Molecular Dynamics A Scalable Model with the Accuracy of Quantum Mechanics https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001
  31. 2018 - Wang et al. - DeePMD-kit A deep learning package for many-body potential energy representation and molecular dynamics https://www.sciencedirect.com/science/article/pii/S0010465518300882
  32. 2019 - Chen et al. - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294
  33. 2019 - Unke and Meuwly - PhysNet A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges https://pubs.acs.org/doi/10.1021/acs.jctc.9b00181
  34. 2019 - Gasteiger et al. - Directional Message Passing for Molecular Graphs https://arxiv.org/abs/2003.03123
  35. 2020 - Hermann et al. - Deep-neural-network solution of the electronic Schrödinger equation https://www.nature.com/articles/s41557-020-0544-y
  36. 2020 - Pfau et al. - Ab initio solution of the many-electron Schrodinger equation with deep neural networks https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033429
  37. 2022 - Batzner et al. - E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials https://www.nature.com/articles/s41467-022-29939-5
  38. 2022 - Glehn et al. - A Self-Attention Ansatz for Ab-initio Quantum Chemistry https://arxiv.org/abs/2211.13672
  39. ……

三、综述

  1. 2016 - Behler - Perspective Machine learning potentials for atomistic simulations https://pubs.aip.org/aip/jcp/article/145/17/170901/195141/
  2. 2017 - Ramprasad et al. - Machine learning in materials informatics recent applications and prospects https://www.nature.com/articles/s41524-017-0056-5
  3. 2017 - Cheng et al. - 量子纠缠:从量子物质态到深度学习 https://wuli.iphy.ac.cn/cn/article/id/70504
  4. 2018 - Butler et al. - Machine learning for molecular and materials science https://www.nature.com/articles/s41586-018-0337-2
  5. 2018 - Dunjko and Briegel - Machine learning & artificial intelligence in the quantum domain a review of recent progress https://iopscience.iop.org/article/10.1088/1361-6633/aab406
  6. 2019 - Carleo et al. - Machine learning and the physical sciences https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002
  7. 2019 - Schmidt et al. - Recent advances and applications of machine learning in solid-state materials science https://www.nature.com/articles/s41524-019-0221-0
  8. 2019 - Schleder et al. - From DFT to machine learning recent approaches to materials science–a review https://iopscience.iop.org/article/10.1088/2515-7639/ab084b
  9. 2019 - Mehta et al. - A high-bias, low-variance introduction to Machine Learning for physicists https://www.sciencedirect.com/science/article/pii/S0370157319300766
  10. 2020 - Carrasquilla - Machine learning for quantum matter https://www.tandfonline.com/doi/full/10.1080/23746149.2020.1797528
  11. 2020 - Bedolla et al. - Machine learning for condensed matter physics https://iopscience.iop.org/article/10.1088/1361-648X/abb895
  12. 2021 - Carrasquilla and Torlai - How To Use Neural Networks To Investigate Quantum Many-Body Physics https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.040201
  13. 2021 - Karniadakis et al. - Physics-informed machine learning https://www.nature.com/articles/s42254-021-00314-5
  14. 2022 - Vivas et al. - Neural-Network Quantum States A Systematic Review https://arxiv.org/abs/2204.12966
  15. 2024 - Lange et al. - From Architectures to Applications A Review of Neural Quantum States https://arxiv.org/abs/2402.09402
  16. 2024 - Suresh et al. - Revolutionizing physics a comprehensive survey of machine learning applications https://www.frontiersin.org/articles/10.3389/fphy.2024.1322162/full
  17. ……
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