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无机晶体材料因其优异的物理和化学特性，在多个领域展现出广泛的应用潜力。弹性性质（如体积模量和剪切模量）对预测材料的电导率、热导率及力学性能具有重要作用，然而，传统实验测量方法存在成本高、周期长等问题。随着计算方法的进步，理论模拟逐渐成为独立于实验的研究方法。近年来，基于图神经网络的机器学习方法在无机晶体材料的弹性性质预测中取得了显著成果，尤其是晶体图卷积神经网络（CGCNN）在材料数据的预测和扩展方面表现出色。本研究利用从Matbench v0.1数据集中收集的10,987个材料的体积模量和剪切模量数据，训练了两个CGCNN模型，基于预训练的模型成功实现了对80,664个无机晶体结构弹性模量的预测。为保证数据质量，筛选了材料电子带隙在0.1至3.0 eV之间，并去除了含有放射性元素的化合物。预测数据来源于两个主要数据集：一是从Materials Project数据库中筛选出的54,359个晶体结构，构成MPED弹性数据集；二是Merchant等通过深度学习和图神经网络方法发现的26,305种晶体结构，构成NED弹性数据集。最终，本研究预测了80,664种无机晶体的体积模量和剪切模量，丰富了现有的材料弹性数据资源，并为材料设计提供了更多的数据支持。本文数据集可在科学数据银行中访问获取https://doi.org/10.57760/sciencedb.j00213.00104。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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Keywords:
- Inorganic crystal materials /
- Elastic modulus /
- Machine learning
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