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RESEARCH PAPER · Physical Review Research · 2020

Unsupervised learning using topological data augmentation

Oleksandr Balabanov, Mats Granath

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42 direct citing works

Unsupervised learning using topological data augmentationOriginalUnsupervised Machine Learning and Band Topology159Interpreting machine learning of topological quantum phase transitions45Topological quantum phase transitions retrieved through unsupervised machine learning94Learning phase transitions in the ferrimagnetic GdFeCo alloy0Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands5Finding hidden order in spin models with persistent homology30Interpretable Machine Learning for the Thermodynamic Phases in 2D XY Model0Conservation laws and spin system modeling through principal component analysis6Machine learning topological invariants of non-Hermitian systems38Engineering topological phases guided by statistical and machine learning methods13Topological persistence machine of phase transitions35Unsupervised Learning of Non-Hermitian Topological Phases47Interpretable and unsupervised phase classification27Bioactivity characterization of herbal molecules9Understanding topological phases of matter with statistical methods0Machine learning of phase transitions in nonlinear polariton lattices23Variational autoencoder analysis of Ising model statistical distributions and phase transitions7Unsupervised identification of Floquet topological phase boundaries9Unsupervised learning of topological phase diagram using topological data analysis16A combined clustering/symbolic regression framework for fluid property prediction36Experimental unsupervised learning of non-Hermitian knotted phases with solid-state spins51Framework for Contrastive Learning Phases of Matter Based on Visual Representations6Unsupervised Data-Driven Classification of Topological Gapped Systems with Symmetries22Unsupervised learning of interacting topological phases from experimental observables6Quaternion-based machine learning on topological quantum systems4Adversarial machine learning phases of matter7Exponentially improved efficient machine learning for quantum many-body states with provable guarantees8Generating Topologically and Geometrically Diverse Manifold Data in Dimensions Four and Below1Reinforcement learning for optimizing magnetic skyrmion creation0Unsupervised learning of non-Abelian multi-gap topological phases0Phase diagram determination using machine learning methods in materials science and engineering: a review3Unsupervised classification of non-Hermitian topological phases under symmetries0Quantum circuit complexity and unsupervised machine learning of topological order3

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  1. Unsupervised Machine Learning and Band Topology

    Physical Review Letters · 2020-06-01

    Mathias S. Scheurer, Robert-Jan Slager

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  2. Interpreting machine learning of topological quantum phase transitions

    Physical Review Research · 2020-06-04

    Yi Zhang, Paul Ginsparg, Eun-Ah Kim

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  3. Topological quantum phase transitions retrieved through unsupervised machine learning

    Physical review. B./Physical review. B · 2020-10-29

    Yanming Che, Clemens Gneiting, Tao Liu, Franco Nori

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  4. Learning phase transitions in the ferrimagnetic GdFeCo alloy

    Unknown journal · 2020-11-14

    N.A. Koritsky, Aleksey K. Fedorov, S. V. Solov’yov, Anatoly K. Zvezdin

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  5. Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands

    Machine Learning Science and Technology · 2020-11-20

    Oleksandr Balabanov, Mats Granath

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  6. Finding hidden order in spin models with persistent homology

    Physical Review Research · 2020-12-02

    Bart Olsthoorn, Johan Hellsvik, Alexander V. Balatsky

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  7. Interpretable Machine Learning for the Thermodynamic Phases in 2D XY Model

    SSRN Electronic Journal · 2021-01-01

    Jintao Yang, Junpeng Cao

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  8. Conservation laws and spin system modeling through principal component analysis

    Computer Physics Communications · 2021-01-14

    David O. Yevick

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  9. Machine learning topological invariants of non-Hermitian systems

    Physical Review A · 2021-01-25

    Lingfeng Zhang, Ling-Zhi Tang, Zhi-Hao Huang, Guo-Qing Zhang, Wei Huang, Dan-Wei Zhang

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  10. Engineering topological phases guided by statistical and machine learning methods

    Physical Review Research · 2021-02-10

    Thomas Mertz, Roser Valentí

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  11. Topological persistence machine of phase transitions

    Physical review. E · 2021-05-21

    Quoc Hoan Tran, Mark Chen, Yoshihiko Hasegawa

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  12. Unsupervised Learning of Non-Hermitian Topological Phases

    Physical Review Letters · 2021-06-16

    Li-Wei Yu, Dong-Ling Deng

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  13. Interpretable and unsupervised phase classification

    Physical Review Research · 2021-07-15

    Julian Arnold, Frank Schäfer, Martin Žonda, Axel U. J. Lode

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  14. Bioactivity characterization of herbal molecules

    Elsevier eBooks · 2021-10-12

    Leticia Félix-Cuencas, Emilio Andrés Delis Hechavarría, Alexandra Jarro, Ixchel Parola-Contreras, Axel Escamilla-García, Irineo Torres‐Pacheco, Juan Fernando García‐Trejo, Genaro M. Soto-Zarazúa, Ramón Gerardo Guevara-González

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  15. Understanding topological phases of matter with statistical methods

    Unknown journal · 2022-01-01

    Mertz, Thomas

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  16. Machine learning of phase transitions in nonlinear polariton lattices

    Communications Physics · 2022-01-10

    Daria Zvyagintseva, Helgi Sigurðsson, Valerii K. Kozin, Ivan V. Iorsh, Ivan Andreevich Shelykh, Ульянцев Владимир Игоревич, Oleksandr Kyriienko

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  17. Variational autoencoder analysis of Ising model statistical distributions and phase transitions

    The European Physical Journal B · 2022-03-01

    David O. Yevick

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  18. Unsupervised identification of Floquet topological phase boundaries

    Physical Review Research · 2022-03-28

    Nannan Ma, Jiangbin Gong

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  19. Unsupervised learning of topological phase diagram using topological data analysis

    Physical review. B./Physical review. B · 2022-05-10

    Sungjoon Park, Yoonseok Hwang, Bohm‐Jung Yang

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  20. A combined clustering/symbolic regression framework for fluid property prediction

    Physics of Fluids · 2022-05-21

    Filippos D. Sofos, Avraam Charakopoulos, Konstantinos Papastamatiou, Theodoros E. Karakasidis

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  21. Experimental unsupervised learning of non-Hermitian knotted phases with solid-state spins

    npj Quantum Information · 2022-09-24

    Yefei Yu, Li-Wei Yu, Wengang Zhang, Huili Zhang, Xiaolong Ouyang, Yanqing Liu, Dong-Ling Deng, L.-M. Duan

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  22. Framework for Contrastive Learning Phases of Matter Based on Visual Representations

    Chinese Physics Letters · 2023-01-01

    Xiao-Qi Han, Sheng-Song Xu, Zhen Feng, Rong-Qiang He, Zhong-Yi Lu

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  23. Unsupervised Data-Driven Classification of Topological Gapped Systems with Symmetries

    Physical Review Letters · 2023-01-18

    Yang Long, Baile Zhang

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  24. Unsupervised learning of interacting topological phases from experimental observables

    Fundamental Research · 2023-01-20

    Li-Wei Yu, Shun-Yao Zhang, Pei-Xin Shen, Dong-Ling Deng

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  25. Quaternion-based machine learning on topological quantum systems

    Machine Learning Science and Technology · 2023-03-01

    Min-Ruei Lin, Wan-Ju Li, Shin-Ming Huang

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  26. Adversarial machine learning phases of matter

    Quantum Frontiers · 2023-11-22

    Si Jiang, Sirui Lu, Dong-Ling Deng

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  27. Exponentially improved efficient machine learning for quantum many-body states with provable guarantees

    Physical Review Research · 2024-07-08

    Yanming Che, Clemens Gneiting, Franco Nori

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  28. Generating Topologically and Geometrically Diverse Manifold Data in Dimensions Four and Below

    International Conference on Digital Image Computing: Techniques and Applications (DICTA) · 2024-11-27

    Khalil Mathieu Hannouch, Stephan K. Chalup

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  29. Reinforcement learning for optimizing magnetic skyrmion creation

    Nanotechnology · 2025-06-08

    Xiuzhu Wang, Zhihua Xiao, Xuezhao Wu, Yan Zhou, Qiming Shao

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  30. Unsupervised learning of non-Abelian multi-gap topological phases

    New Journal of Physics · 2025-09-01

    Xiangxu He, Ruo-Yang Zhang, Xiaohan Cui, Lei Zhang, Che Ting Chan

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  31. Phase diagram determination using machine learning methods in materials science and engineering: a review

    The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · 2025-09-17

    Abhishek Kumar Thakur, Sanyam Nitin Totade, Krishna Muralidharan, Monojit Dutta

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  32. Unsupervised classification of non-Hermitian topological phases under symmetries

    National Science Review · 2025-11-26

    Yang Long, Haoran Xue, Baile Zhang

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  33. Quantum circuit complexity and unsupervised machine learning of topological order

    Nature Communications · 2026-04-08

    Yanming Che, Clemens Gneiting, Xiaoguang Wang, Franco Nori

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