Due to their excellent physical and chemical properties, inorganic crystal materials have shown extensive application potential in many fields. Elastic properties such as shear modulus and bulk modulus play an important role in predicting the electrical conductivity, thermal conductivity and mechanical properties of materials. However, the traditional experimental measurement method has some problems such as high cost and low effciency. With the development of computational methods, theoretical simulation has gradually become an effective alternative to experiments. In recent years, graph neural networkbased machine learning methods have achieved remarkable results in the prediction of elastic properties of inorganic crystal materials, especially crystal graph convolutional neural networks (CGCNN), which perform well in the prediction and expansion of material data. In this study, two CGCNN models were trained using the shear modulus and bulk modulus data of 10,987 materials collected in the Matbench v0.1 dataset. These models show high accuracy and good generalization ability in predicting shear modulus and bulk modulus. The mean absolute error (MAE) is less than 13 and the coeffcient of determination (R2) is close to 1.We then screened two datasets with a band gap between 0.1 and 3.0 eV and excluded compounds containing radioactive elements. The dataset consists of two parts: The first part is composed of 54,359 crystal structures selected from the Materials Project database, which constitutes the MPED dataset; The second part is the 26,305 crystal structures discovered by Merchant et al. (Nature 624 , 80 (2023) through deep learning and graph neural network methods, which constitute the NED dataset. Finally, the shear modulus and bulk modulus of 80,664 inorganic crystals are predicted in this study, which enriches the existing material elastic data resources and provides more data support for material design. This dataset is publicly available and can be accessed via the Science Data Bank at https://doi.org/10.57760/sciencedb.j00213.00104.
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