This article presents a machine learning-based method for accurately predicting the crystal structures of two-dimensional hybrid organic-inorganic perovskites (HOIPs). The researchers developed a machine learning interatomic potential (MLIP) based on the MACE architecture, which was trained on experimental data and tested for efficiency in predicting HOIP structures. The approach combines random structure search and MLIP to predict new materials, successfully synthesizing a new perovskite matching their predicted structure. This method enables efficient screening of potential perovskite materials for applications in optoelectronics and photovoltaics.
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https://pubs.acs.org/doi/10.1021/jacs.4c06549
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