Karimitari Nima, Baldwin William J, Muller Evan W, Bare Zachary J L, Kennedy W Joshua, Csányi Gábor, Sutton Christopher
Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States.
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, U.K.
J Am Chem Soc. 2024 Oct 9;146(40):27392-27404. doi: 10.1021/jacs.4c06549. Epub 2024 Sep 30.
Low-dimensional hybrid organic-inorganic perovskites (HOIPs) are promising electronically active materials for light absorption and emission. The design space of HOIPs is extremely large, as a variety of organic cations can be combined with different inorganic frameworks. This not only allows for tunable electronic and mechanical properties but also necessitates the development of new tools for in silico high throughput analysis of candidate materials. In this work, we present an accurate, efficient, and widely applicable machine learning interatomic potential (MLIP) trained on 86 diverse experimentally reported HOIP materials. This MLIP was tested on 73 experimentally reported perovskite compositions and achieves a high accuracy, relative to density functional theory (DFT). We also introduce a novel random structure search algorithm designed for the crystal structure prediction of 2D HOIPs. The combination of MLIP and the structure search algorithm reliably recovers the crystal structure of 14 known 2D perovskites by specifying only the organic molecule and inorganic cation/halide. Performing this crystal structure search with ab initio methods would be computationally prohibitive but is relatively inexpensive with the MLIP. Finally, the developed procedure is used to predict the structure of a totally new HOIP with cation (-1,3-cyclohexanediamine). Subsequently, the new compound was synthesized and characterized, which matches the predicted structure, confirming the accuracy of our method. This capability will enable the efficient and accurate screening of thousands of combinations of organic cations and inorganic layers for further investigation.
低维有机-无机杂化钙钛矿（HOIPs）是用于光吸收和发射的有前景的电子活性材料。HOIPs的设计空间极大，因为多种有机阳离子可与不同的无机骨架相结合。这不仅允许实现可调节的电子和机械性能，还需要开发用于候选材料的计算机高通量分析的新工具。在这项工作中，我们展示了一种在86种不同的实验报道的HOIP材料上训练的准确、高效且广泛适用的机器学习原子间势（MLIP）。相对于密度泛函理论（DFT），该MLIP在73种实验报道的钙钛矿组成上进行了测试，并实现了高精度。我们还引入了一种专为二维HOIPs的晶体结构预测设计的新型随机结构搜索算法。MLIP与结构搜索算法的结合通过仅指定有机分子和无机阳离子/卤化物，可靠地恢复了14种已知二维钙钛矿的晶体结构。用从头算方法进行这种晶体结构搜索在计算上是 prohibitive的，但使用MLIP则相对便宜。最后，所开发的程序用于预测一种具有阳离子（-1,3-环己二胺）的全新HOIP的结构。随后，合成并表征了该新化合物，其与预测结构匹配，证实了我们方法的准确性。这种能力将使我们能够高效且准确地筛选数千种有机阳离子和无机层的组合，以供进一步研究。
