钙钛矿(结构)
带隙
材料科学
半导体
卤化物
杠杆(统计)
宽禁带半导体
光电子学
纳米技术
化学物理
计算机科学
无机化学
化学
结晶学
机器学习
作者
Tonghui Wang,Ruipeng Li,Hossein Ardekani,Lucía Serrano-Luján,Jiantao Wang,Mahdi Ramezani,Ryan Wilmington,Mihirsinh Chauhan,Robert W. Epps,Kasra Darabi,Boyu Guo,Dali Sun,Milad Abolhasani,Kenan Gündoğdu,Aram Amassian
出处
期刊:Matter
[Elsevier]
日期:2023-09-01
卷期号:6 (9): 2963-2986
被引量:3
标识
DOI:10.1016/j.matt.2023.06.040
摘要
The vast chemical space of emerging semiconductors, like metal halide perovskites, and their varied requirements for semiconductor applications have rendered trial-and-error environmentally unsustainable. In this work, we demonstrate RoboMapper, a materials acceleration platform (MAP), that achieves 10-fold research acceleration by formulating and palletizing semiconductors on a chip, thereby allowing high-throughput (HT) measurements to generate quantitative structure-property relationships (QSPRs) considerably more efficiently and sustainably. We leverage the RoboMapper to construct QSPR maps for the mixed ion FA1−yCsyPb(I1−xBrx)3 halide perovskite in terms of structure, bandgap, and photostability with respect to its composition. We identify wide-bandgap alloys suitable for perovskite-Si hybrid tandem solar cells exhibiting a pure cubic perovskite phase with favorable defect chemistry while achieving superior stability at the target bandgap of ∼1.7 eV. RoboMapper’s palletization strategy reduces environmental impacts of data generation in materials research by more than an order of magnitude, paving the way for sustainable data-driven materials research.
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