New tolerance factor to predict the stability of perovskite oxides and halides
New tolerance factor to predict the stability of perovskite oxides and halides
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DOI：
10.1126/sciadv.aav0693
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发表时间：
2019-02-01
期刊：
影响因子：
13.6
通讯作者：
Scheffler, Matthias
中科院分区：
文献类型：
--
作者：
Bartel, Christopher J.;Sutton, Christopher;Scheffler, Matthias
预测钙钛矿结构的稳定性仍然是发现用于许多应用的新功能材料（包括光催化剂和电催化剂）的长期挑战。我们开发了一个准确的，物理上可解释的一维容差因子'r，使用基于SISSO（确定独立筛选和稀疏化算子）的新型数据分析方法，正确预测了576 ABX（3）材料（X = O2-，F-，Cl-，Br-，I-）实验数据集的92%化合物为钙钛矿或非钙钛矿。τ被示出为在1034个实验实现的单钙钛矿和双钙钛矿的训练集（91%准确度）之外进行推广，并且被应用于识别23，314个新的双钙钛矿（A（2）BB ′ X-6），其通过它们作为钙钛矿稳定的概率进行排名。这项工作指导了实验学家和理论家最有可能成功合成钙钛矿，并展示了一种描述符识别方法，该方法可以扩展到钙钛矿稳定性预测之外的任意应用。
Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, 'r, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX(3) materials (X = O2-, F-, Cl-, Br-, I-) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). tau is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A(2)BB'X-6) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.