Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are...

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Veröffentlicht in:	Joule 2019-06, Vol.3 (6), p.1437-1451
Hauptverfasser:	Sun, Shijing, Hartono, Noor T.P., Ren, Zekun D., Oviedo, Felipe, Buscemi, Antonio M., Layurova, Mariya, Chen, De Xin, Ogunfunmi, Tofunmi, Thapa, Janak, Ramasamy, Savitha, Settens, Charles, DeCost, Brian L., Kusne, Aaron G., Liu, Zhe, Tian, Siyu I.P., Peters, Ian Marius, Correa-Baena, Juan-Pablo, Buonassisi, Tonio
Format:	Artikel
Sprache:	eng
Schlagworte:	accelerated materials development lead free machine-learning-assisted diagnosis perovskites photovoltaics
Online-Zugang:	Volltext
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