Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a promising class of electronically active materials for both light absorption and emission. The design space of HOIPs is extremely large, since a diverse space of organic cations can be combined with different inorganic frameworks. This immense design space allows for tunable electronic and mechanical properties, but also necessitates the development of new tools for in silico high throughput analysis of candidate structures. In this work, we present an accurate, efficient, transferable and widely applicable machine learning interatomic potential (MLIP) for predicting the structure of new 2D HOIPs. Using the MACE architecture, an MLIP is trained on 86 diverse experimentally reported HOIP structures. The model is tested on 73 unseen perovskite compositions, and achieves chemical accuracy with respect to the reference electronic structure method. Our model is then combined with a simple random structure search algorithm to predict the structure of hypothetical HOIPs given only the proposed composition. Success is demonstrated by correctly and reliably recovering the crystal structure of a set of experimentally known 2D perovskites. Such a random structure search is impossible with ab initio methods due to the associated computational cost, but is relatively inexpensive with the MACE potential. Finally, the procedure is used to predict the structure formed by a new organic cation with no previously known corresponding perovskite. Laboratory synthesis of the new hybrid perovskite confirms the accuracy of our prediction. This capability, applied at scale, enables efficient screening of thousands of combinations of organic cations and inorganic layers.

翻译：低维杂化有机-无机钙钛矿（HOIPs）是一类兼具光吸收与发射性能的 promising 电子活性材料。HOIPs的设计空间极其广阔，因为种类繁多的有机阳离子可与不同的无机骨架相互组合。这一巨大的设计空间虽赋予材料可调谐的电子与机械性能，但也亟需开发面向候选结构的高通量计算机分析新工具。本研究提出一种精确、高效、可迁移且广泛适用的机器学习原子间势（MLIP），用于预测新型二维HOIPs的结构。基于MACE架构，该MLIP在86种实验报道的多样化HOIP结构上完成训练。模型在73种未见过的钙钛矿组分上经测试，达到了参考电子结构方法级别的化学精度。随后，我们将模型与简单随机结构搜索算法结合，仅依据假设组分即可预测未知HOIPs的结构。通过正确可靠地恢复一组实验已知二维钙钛矿的晶体结构，成功验证了本方法的有效性。这种随机结构搜索由于计算成本高昂而无法通过从头计算方法实现，但使用MACE势能可相对经济地完成。最后，该方法被用于预测一种新型有机阳离子（尚无对应已知钙钛矿）所形成结构。通过实验室合成该新型杂化钙钛矿，证实了我们预测的准确性。规模化应用此项技术，能够实现对数千种有机阳离子与无机层组合的高效筛选。