论文标题
2D混合钙钛矿材料的数据库：机器学习预测的晶体结构，带隙和原子部分费用的开放式收集
Database of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning
论文作者
论文摘要
我们描述了使用二维（2D）钙钛矿样晶体结构的实验研究的第一个开放式访问数据库。该数据库包括515种化合物，其中包含180种不同的有机阳离子，10种金属（PB，SN，BI，CD，CU，FE，FE，GE，MN，MN，PD，SB）和3个卤素（I，BR，BR，CL）到目前为止已知，并将定期更新。该数据库包含对结构的几何和晶体化学分析，这些分析对于揭示此类化合物的定量结构 - 特性关系很有用。我们表明，间隔有机阳离子到无机层中的渗透深度和M-x-M键角增加了无机层的数量（N）。机器学习模型是在数据库上开发和训练的，以预测在0.1 eV之内的频带隙。培训了另一个机器学习模型，以预测0.01 e以内的原子部分电荷。我们表明，随着n的增加，带隙的预测值降低，并且单层钙棍蛋白酶的M-x-m角增加。通常，提出的数据库和机器学习模型被证明是用于新2D混合钙钛矿材料合理设计的有用工具。
We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, Sb) and 3 halogens (I, Br, Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful to reveal quantitative structure-property relationships for this class of compounds. We show that the penetration depth of spacer organic cation into the inorganic layer and M-X-M bond angles increase in the number of inorganic layers (n). The machine learning model is developed and trained on the database, for the prediction of a band gap with accuracy within 0.1 eV. Another machine learning model is trained for the prediction of atomic partial charges with accuracy within 0.01 e. We show that the predicted values of band gaps decrease with an increase of the n and with an increase of M-X-M angles for single-layered perovskites. In general, the proposed database and machine learning models are shown to be useful tools for the rational design of new 2D hybrid perovskite materials.
