Rapid Data‐Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and er...

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Vydáno v:	Advanced materials (Weinheim) Ročník 35; číslo 16; s. e2208772 - n/a
Hlavní autoři:	Lampe, Carola, Kouroudis, Ioannis, Harth, Milan, Martin, Stefan, Gagliardi, Alessio, Urban, Alexander S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Germany Wiley Subscription Services, Inc 01.04.2023
Témata:	Algorithms Bayesian optimization data‐efficient optimization Dispersions Functional materials Gaussian processes halide perovskites Homogeneity Machine learning Material properties Materials science Monolayers Nanocrystals Optimization Parameters Perovskites Photoluminescence Platelets (materials) photoluminescence data-efficient optimization nanocrystals Gaussian processes halide perovskites Bayesian optimization machine learning
ISSN:	0935-9648, 1521-4095, 1521-4095
On-line přístup:	Získat plný text
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