Zhao Jie, Wang Xiaoyan
College of Chemical Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, People's Republic of China.
School of Information Engineering, Nanjing Audit University, Nanjing, Jiangsu 211815, People's Republic of China.
ACS Omega. 2022 Mar 16;7(12):10483-10491. doi: 10.1021/acsomega.2c00002. eCollection 2022 Mar 29.
Perovskite oxides are attractive candidates for various scientific applications because of their outstanding structure flexibilities and attractive physical and chemical properties. However, labor-intensive and high-cost experimental and density functional theory calculation approaches are normally used to screen candidate perovskites. Herein, a machine learning method is employed to identify perovskites from ABO combinations formulated as constraint satisfaction problems based on the restrictions of charge neutrality and Goldschmidt tolerance factor. By eliminating five features based on their correlation and importance, 16 features refined from 21 features are employed to describe 343 known ABO compounds for perovskite formability and stability model training. It is found that the top three features for predicting formability are structural features of the A-O bond length, tolerance, and octahedral factors, whereas the top nine features for predicting the stability are elemental and structural features related to the B-site elements. The precision and recall of the two models are 0.983, 1.00 and 0.971, 0.943, respectively. The formability prediction model categorizes 2229 ABO combinations into 1373 perovskites and 856 nonperovskites, whereas the stability prediction model distinguishes 430 stable perovskites from 1799 unstable ones. Three hundred thirty-eight combinations are recognized as both formable and stable perovskites for future investigation.
钙钛矿氧化物因其出色的结构灵活性以及吸引人的物理和化学性质，成为各种科学应用中颇具吸引力的候选材料。然而，通常采用劳动强度大且成本高的实验方法和密度泛函理论计算方法来筛选候选钙钛矿。在此，我们采用一种机器学习方法，基于电荷中性和戈德施密特容忍因子的限制，将ABO组合识别为约束满足问题来确定钙钛矿。通过基于相关性和重要性消除五个特征，从21个特征中提炼出16个特征，用于描述343种已知的ABO化合物，以训练钙钛矿形成性和稳定性模型。研究发现，预测形成性的前三个特征是A - O键长、容忍度和八面体因子的结构特征，而预测稳定性的前九个特征是与B位元素相关的元素和结构特征。这两个模型的精度和召回率分别为0.983、1.00和0.971、0.943。形成性预测模型将2229种ABO组合分为1373种钙钛矿和856种非钙钛矿，而稳定性预测模型则从1799种不稳定的钙钛矿中区分出430种稳定的钙钛矿。338种组合被认定为既具有形成性又具有稳定性的钙钛矿，可供未来研究。
