With the rapid development of artificial intelligence and machine learning (ML) methods, materials science is rapidly entering the era of data-driven materials informatics. ML models serve as the most crucial component, closely bridging material structure and properties. There is a considerable difference in the prediction performance of different ML methods for material systems. Herein, we evaluated models of three categories (linear, kernel, and nonlinear method), with twelve ML algorithms commonly used in the materials field. In addition, halide perovskite was chosen as an example to evaluate the fitting performance of different models. We constructed a total dataset of 540 halide perovskites and 72 features, with formation energy and bandgap as target properties. We found that different categories of ML models show similar trends for different target properties. Among them, the difference between the models is enormous for the formation energy, with the coefficient of determination (R2) range: 0.69~0.953, while the fitting performance between the models whose R2 range: 0.941~0.997 is closer for bandgap. The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap. It shows that the nonlinear-ensemble model, constructed by combining multiple weak learners, effectively describes the nonlinear relationship between material features and target property. In addition, the eXtreme gradient boosting decision tree model performs the most superior results among all the models and the searching of two new descriptors that are crucial for formation energy and bandgap. Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.

Hydrofluorination can efficiently enhance the piezoelectric response of 2D penta-graphene.

The considerable thermal expansion of halide perovskites is one of the challenges to device stability, yet the physical origin and modulation strategy remain unclear. Herein, we report first-principles calculations of the thermal properties of halide perovskites at 300 K using oxides as a reference. We found that the large thermal expansion of halide perovskites can mainly be attributed to their low bulk modulus and volumetric heat capacity because of the soft crystal lattice, whereas composition-dependent anharmonicity emerges as the most important factor in determining thermal expansion with the same structure. We discovered that thermal expansion of halide perovskites can be decreased by weakening the B−X bond to promote the octahedral anharmonicity. We further proposed an effective thermal expansion coefficient descriptor of halide perovskites with a Pearson correlation coefficient of nearly −80%. Our findings provide insights into the underlying mechanisms and chemical trends in the thermal expansion behavior of halide perovskites.

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