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 BV]
日期:2023-09-01
卷期号:6 (9): 2963-2986
被引量:8
标识
DOI:10.1016/j.matt.2023.06.040
摘要
Summary
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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