Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints
Abstract Two-dimensional materials with flat electronic bands are promising for realising exotic quantum phenomena such as unconventional superconductivity and nontrivial topology. However, exploring their vast chemical space is a significant challenge. Here we introduce elf, an unsupervised convolu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-01936-2 |
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| author | Henry Kelbrick Pentz Thomas Warford Ivan Timokhin Hongpeng Zhou Qian Yang Anupam Bhattacharya Artem Mishchenko |
| author_facet | Henry Kelbrick Pentz Thomas Warford Ivan Timokhin Hongpeng Zhou Qian Yang Anupam Bhattacharya Artem Mishchenko |
| author_sort | Henry Kelbrick Pentz |
| collection | DOAJ |
| description | Abstract Two-dimensional materials with flat electronic bands are promising for realising exotic quantum phenomena such as unconventional superconductivity and nontrivial topology. However, exploring their vast chemical space is a significant challenge. Here we introduce elf, an unsupervised convolutional autoencoder that encodes electronic band structure images into fingerprint vectors, enabling the autonomous clustering of materials by electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the fingerprint space then uncovers hidden chemical trends and identifies promising candidates based on similarities to well-studied exemplars. This approach complements high-throughput ab initio methods by rapidly screening candidates and guiding further investigations into the mechanisms underlying flat-band physics. The elf autoencoder is a powerful tool for autonomous discovery of unexplored flat-band materials, enabling unbiased identification of compounds with desirable electronic properties across the 2D chemical space. |
| format | Article |
| id | doaj-art-286d73fd19cd464db9c8ed422403d6ef |
| institution | Kabale University |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-286d73fd19cd464db9c8ed422403d6ef2025-01-19T12:26:19ZengNature PortfolioCommunications Physics2399-36502025-01-018111010.1038/s42005-025-01936-2Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprintsHenry Kelbrick Pentz0Thomas Warford1Ivan Timokhin2Hongpeng Zhou3Qian Yang4Anupam Bhattacharya5Artem Mishchenko6Department of Physics and Astronomy, the University of ManchesterDepartment of Physics and Astronomy, the University of ManchesterDepartment of Physics and Astronomy, the University of ManchesterDepartment of Computer Science, the University of ManchesterDepartment of Physics and Astronomy, the University of ManchesterDepartment of Physics and Astronomy, the University of ManchesterDepartment of Physics and Astronomy, the University of ManchesterAbstract Two-dimensional materials with flat electronic bands are promising for realising exotic quantum phenomena such as unconventional superconductivity and nontrivial topology. However, exploring their vast chemical space is a significant challenge. Here we introduce elf, an unsupervised convolutional autoencoder that encodes electronic band structure images into fingerprint vectors, enabling the autonomous clustering of materials by electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the fingerprint space then uncovers hidden chemical trends and identifies promising candidates based on similarities to well-studied exemplars. This approach complements high-throughput ab initio methods by rapidly screening candidates and guiding further investigations into the mechanisms underlying flat-band physics. The elf autoencoder is a powerful tool for autonomous discovery of unexplored flat-band materials, enabling unbiased identification of compounds with desirable electronic properties across the 2D chemical space.https://doi.org/10.1038/s42005-025-01936-2 |
| spellingShingle | Henry Kelbrick Pentz Thomas Warford Ivan Timokhin Hongpeng Zhou Qian Yang Anupam Bhattacharya Artem Mishchenko Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints Communications Physics |
| title | Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints |
| title_full | Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints |
| title_fullStr | Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints |
| title_full_unstemmed | Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints |
| title_short | Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints |
| title_sort | elf autoencoder for unsupervised exploration of flat band materials using electronic band structure fingerprints |
| url | https://doi.org/10.1038/s42005-025-01936-2 |
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