Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening

Zhang, Zhaosheng; Zhang, Yingjie; Liu, Sijia

doi:10.1016/j.commatsci.2024.112889

Computational Materials Science

2024

DOI: 10.1016/j.commatsci.2024.112889

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Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening

Zhaosheng Zhang,

Yingjie Zhang,

Sijia Liu

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2024

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Cited by 3 publications

References 41 publications

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Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

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Dehimi

et al. 2024

J Opt

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Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

Mammeri,

Bencherif,

Dehimi

et al. 2024

J Opt

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Enhanced Design of Kesterite Solar Cells through High-Throughput Screening and Machine Learning Approaches

Zhang,

Xiong,

Liu

2024

J. Phys. Chem. Lett.

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Studying the Thermodynamic Phase Stability of Organic–Inorganic Hybrid Perovskites Using Machine Learning

Wang,

Feng

et al. 2024

Molecules

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As an important photovoltaic material, organic–inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one of the main challenges limiting their commercial application. However, the search for stable perovskites among the thousands of perovskite materials still faces great challenges. In this work, the energy above the convex hull values of organic–inorganic hybrid perovskites was predicted based on four different machine learning algorithms, namely random forest regression (RFR), support vector machine regression (SVR), XGBoost regression, and LightGBM regression, to study the thermodynamic phase stability of organic–inorganic hybrid perovskites. The results show that the LightGBM algorithm has a low prediction error and can effectively capture the key features related to the thermodynamic phase stability of organic–inorganic hybrid perovskites. Meanwhile, the Shapley Additive Explanation (SHAP) method was used to analyze the prediction results based on the LightGBM algorithm. The third ionization energy of the B element is the most critical feature related to the thermodynamic phase stability, and the second key feature is the electron affinity of ions at the X site, which are significantly negatively correlated with the predicted values of energy above the convex hull (Ehull). In the screening of organic–inorganic perovskites with high stability, the third ionization energy of the B element and the electron affinity of ions at the X site is a worthy priority. The results of this study can help us to understand the correlation between the thermodynamic phase stability of organic–inorganic hybrid perovskites and the key features, which can assist with the rapid discovery of highly stable perovskite materials.

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Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening

Cited by 3 publications

References 41 publications

Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

Enhanced Design of Kesterite Solar Cells through High-Throughput Screening and Machine Learning Approaches

Studying the Thermodynamic Phase Stability of Organic–Inorganic Hybrid Perovskites Using Machine Learning

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