The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Machine learning topological states
Open accessAbstract
Machine learning, the core of artificial intelligence and data science, is a very active field, with vast applications throughout science and technology. Recently, machine learning techniques have been adopted to tackle intricate quantum many-body problems and phase transitions. In this work, the authors construct exact mappings from exotic quantum states to machine learning network models. This work shows for the first time that the restricted Boltzmann machine can be used to study both symmetry-protected topological phases and intrinsic topological order. The exact results are expected to provide a substantial boost to the field of machine learning of phases of matter.
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Cites
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Cited by
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,607
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
Neural-network quantum state tomography
Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R., & Carleo, G. (2018) · Nature Physics · doi:10.1038/s41567-018-0048-5 · Cited by 843
All-optical neural network with nonlinear activation functions
Zuo, Y., Li, B., Zhao, Y., Jiang, Y., Chen, Y.-C., Chen, P., Jo, G.-B., Liu, J., & Du, S. (2019) · Optica · doi:10.1364/optica.6.001132 · Cited by 474
Recent advances in 2D, 3D and higher-order topological photonics
Kim, M., Jacob, Z., & Rho, J. (2020) · Light: Science & Applications · doi:10.1038/s41377-020-0331-y · Cited by 470
Metadata
- Authors
- Deng, D.-L., Li, X., & Das Sarma, S.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2017-11-22
- Volume
- 96
- Issue
- 19
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2769287225
- Cited by
- 290
- References
- 17
- FWCI
- 17.26
- Type
- journal article
- Language
- en
- Open access
- green
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- Deng, D.-L., Li, X., & Das Sarma, S. (2017). Machine learning topological states. Physical Review B. https://doi.org/10.1103/physrevb.96.195145
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
