Discovering phase transitions with unsupervised learning
Open accessAbstract
Unsupervised learning is a discipline of machine learning which aims at discovering patterns in large data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many-body systems. Starting with raw spin configurations of a prototypical Ising model, we use principal component analysis to extract relevant low-dimensional representations of the original data and use clustering analysis to identify distinct phases in the feature space. This approach successfully finds physical concepts such as the order parameter and structure factor to be indicators of a phase transition. We discuss the future prospects of discovering more complex phases and phase transitions using unsupervised learning techniques.
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An example of a successful inference along the line of unboxing has been provided recently (Wang, 2016).
“An example of a successful inference along this line has been provided recently (Wang, 2016).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Wang, 2016
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · see citing works
“The factories of the future are likely to utilise AI technology extensively, as production becomes more automated and industry migrates to a more intelligent platform using AI and cyber physical systems (Wang & Wang, 2016).”
BackgroundMethodInfluentialSolving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215 · see citing works
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517 · see citing works
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040 · see citing works
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808 · see citing works
Quantum Fisher information matrix and multiparameter estimation
Liu, J., Yuan, H., Lu, X.-M., & Wang, X. (2020) · Journal of Physics A: Mathematical and Theoretical · doi:10.1088/1751-8121/ab5d4d · Cited by 802 · see citing works
Toward an artificial intelligence physicist for unsupervised learning
Wu, T., & Tegmark, M. (2019) · Physical Review E · doi:10.1103/physreve.100.033311 · Cited by 99 · see citing works
Precise measurement of quantum observables with neural-network estimators
Torlai, G., Mazzola, G., Carleo, G., & Mezzacapo, A. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.022060 · Cited by 80 · see citing works
Machine learning phase transitions with a quantum processor
Uvarov, A. V., Kardashin, A. S., & Biamonte, J. D. (2020) · Physical Review A · doi:10.1103/physreva.102.012415 · Cited by 69 · see citing works
- Authors
- Wang, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wang, Lei
- ORCID
- Wang, Lei
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2016-11-02
- Volume
- 94
- Issue
- 19
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2414456771
- Cited by
- 618
- References
- 1
- Citations by year
- 2026: 33 · 2025: 57 · 2024: 60 · 2023: 57 · 2022: 74 · 2021: 91 · 2020: 97 · 2019: 66 · 2018: 52 · 2017: 29 · 2016: 1
- FWCI
- 29.28
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical review. B./Physical review. B (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2016YFA0300603)
- Topics
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- phase transitions, unsupervised learning, Ising model, spin configurations, principal component analysis, clustering, order parameter, structure factor, many-body systems, phase identification
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