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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Veröffentlicht in:Materials discovery Jg. 4; S. 18 - 21
Hauptverfasser: Ueno, Tsuyoshi, Rhone, Trevor David, Hou, Zhufeng, Mizoguchi, Teruyasu, Tsuda, Koji
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2016
Schlagworte:
Bayesian optimization
Global optimization
Materials design
Python library
Global optimization
Bayesian optimization
Python library
Materials design
ISSN:2352-9245, 2352-9245
Online-Zugang:Volltext
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Zusammenfassung:[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.
ISSN:2352-9245
2352-9245
DOI:10.1016/j.md.2016.04.001