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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