Rationalizing Perovskite Data for Machine Learning and Materials Design,The Journal of Physical Chemistry Letters

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Rationalizing Perovskite Data for Machine Learning and Materials Design
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2018-11-27 00:00:00 , DOI: 10.1021/acs.jpclett.8b03232
Qichen Xu _{1,

2} , Zhenzhu Li _{1,

2} , Miao Liu ₃ , Wan-Jian Yin _{1,

2}

Affiliation

Machine learning has been recently used for novel perovskite designs owing to the availability of a large amount of perovskite formability data. Trustworthy results should be based on the valid and reliable data that can reveal the nature of materials as much as possible. In this study, a procedure has been developed to identify the formability of perovskites for all of the compounds with the stoichiometry of ABX₃ and (A′A″)(B′B′′)X₆ that exist in experiments and are stored in the Materials Projects database. Our results have enriched the data of perovskite formability to a large extent and corrected the possible errors of previous data in ABO₃ compounds. Furthermore, machine learning with a multiple models approach has identified the A₂B′B″O₆ compounds that have suspicious formability results in the current experimental data. Therefore, further experimental validation experiments are called for. This work paves a way for cleaning perovskite formability data for reliable machine-learning work in future.

中文翻译：

合理化钙钛矿数据以进行机器学习和材料设计

由于可获得大量钙钛矿可成形性数据，因此机器学习最近已用于新颖的钙钛矿设计。可信赖的结果应该基于有效和可靠的数据，这些数据可以尽可能揭示材料的性质。在这项研究中，已经开发了一种程序，用于鉴定所有存在于实验中并存储在ABX ₃和（A'A''）（B'B''）X ₆的化学计量比的所有化合物的钙钛矿的可成形性。材料项目数据库。我们的结果在很大程度上丰富了钙钛矿可成型性的数据，并纠正了ABO ₃化合物中先前数据的可能误差。此外，采用多模型方法的机器学习已确定A ₂具有可成形性的B′B″ O ₆化合物可在当前实验数据中得出。因此，需要进一步的实验验证实验。这项工作为清洗钙钛矿可成型性数据铺平了道路，以便将来进行可靠的机器学习。

更新日期：2018-11-27

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