Abstract
Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.
Highlights
Accepted: 23 June 2021With the progress of science and technology as well as the development of social economy, people are very active in the research on the development and utilization of various energy resources [1,2,3,4,5,6]
In ABO3 perovskite oxides, the A site is the rare earth or alkaline earth metal ions, which usually stabilize the structure, while the B site is occupied by the smaller transition metal ions [17]
It can be seen that the R2 value of random forest (RF) is the highest, which is 0.7231, and the values of mean absolute errors (MAEs) and mean square error (MSE) are the lowest, which are 0.3731 and 0.2449, respectively
Summary
Accepted: 23 June 2021With the progress of science and technology as well as the development of social economy, people are very active in the research on the development and utilization of various energy resources [1,2,3,4,5,6]. Research has focused on the development of new perovskite materials to improve activity, selectivity, and stability, as well as the development of advanced manufacturing techniques to reduce their cost while ensuring their reliability, safety, and reproducibility [14,15,16]. When A or B sites are partially replaced by other metal ions, anion defects or B sites at different valences can be formed. This improves the properties of the compounds but does not fundamentally change the crystal structure [17]. This kind of composite oxide has gas sensitivity, oxidation catalytic property, conductivity, oxygen permeability, and other properties. Its structure and performance are closely related to the composition of the system [17]
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