COMBO: An efficient Bayesian optimization library for materials science
[Display omitted] In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn...
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| Vydáno v: | Materials discovery Ročník 4; s. 18 - 21 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2016
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| Témata: | |
| ISSN: | 2352-9245, 2352-9245 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open-source python library called COMBO (COMmon Bayesian Optimization library). Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https://github.com/tsudalab/combo. |
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| AbstractList | [Display omitted]
In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open-source python library called COMBO (COMmon Bayesian Optimization library). Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https://github.com/tsudalab/combo. |
| Author | Ueno, Tsuyoshi Rhone, Trevor David Tsuda, Koji Hou, Zhufeng Mizoguchi, Teruyasu |
| Author_xml | – sequence: 1 givenname: Tsuyoshi surname: Ueno fullname: Ueno, Tsuyoshi organization: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5-CB02 Kashiwanoha, Kashiwa 277-8561, Japan – sequence: 2 givenname: Trevor David surname: Rhone fullname: Rhone, Trevor David organization: Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA – sequence: 3 givenname: Zhufeng surname: Hou fullname: Hou, Zhufeng organization: Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan – sequence: 4 givenname: Teruyasu surname: Mizoguchi fullname: Mizoguchi, Teruyasu organization: Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 153-8505, Japan – sequence: 5 givenname: Koji orcidid: 0000-0002-4288-1606 surname: Tsuda fullname: Tsuda, Koji email: tsuda@k.u-tokyo.ac.jp organization: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5-CB02 Kashiwanoha, Kashiwa 277-8561, Japan |
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| Cites_doi | 10.1063/1.2210932 10.1023/A:1008306431147 10.1103/PhysRevB.89.054303 10.1109/5992.998641 10.1021/ci3004682 10.7567/JJAP.55.045502 10.1103/PhysRevLett.115.205901 10.1016/j.commatsci.2012.10.028 10.1016/j.drudis.2014.12.004 10.1038/srep19660 10.1090/S0025-5718-1974-0343558-6 10.1039/a606455h |
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In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven... |
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| Title | COMBO: An efficient Bayesian optimization library for materials science |
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