Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys
Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys
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DOI：
10.1016/j.matt.2023.06.040
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发表时间：
2023-07
期刊：
影响因子：
18.9
通讯作者：
Tonghui Wang;Ruipeng Li;H. Ardekani;L. Serrano-Luján;Jiantao Wang;Mahdi Ramezani;R. Wilmington;Mihirsinh Chauhan;Robert W. Epps;Kasra Darabi;Boyu Guo;Dali Sun;M. Abolhasani;K. Gundogdu;A. Amassian
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文献类型：
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作者：
Tonghui Wang;Ruipeng Li;H. Ardekani;L. Serrano-Luján;Jiantao Wang;Mahdi Ramezani;R. Wilmington;Mihirsinh Chauhan;Robert W. Epps;Kasra Darabi;Boyu Guo;Dali Sun;M. Abolhasani;K. Gundogdu;A. Amassian
金属卤化物钙钛矿等新兴半导体的巨大化学空间，以及它们对半导体应用的不同要求，使得反复试验在环境上不可持续。在这项工作中，我们展示了RoboMapper，这是一个材料加速平台(MAP)，通过在芯片上制定和堆积半导体，实现了10倍的研究加速，从而使高通量(HT)测量能够更高效、更可持续地生成定量结构-性质关系(QSPR)。我们利用RoboMapper构建了混合离子Fa1−yCsyPb(I1−xBrx)3卤化物钙钛矿的结构、带隙和光稳定性的QSPR图。我们确定了适用于钙钛矿-硅混合串联太阳能电池的宽带隙合金，该合金具有纯立方钙钛矿相，具有良好的缺陷化学，同时在∼1.7 eV的目标带隙下获得了优异的稳定性。RoboMapper的堆垛战略将材料研究中数据生成对环境的影响减少了一个数量级以上，为可持续的数据驱动材料研究铺平了道路。
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)3halide 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.