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无机晶体材料因具有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10987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80664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—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54359个晶体结构, 构成MPED弹性数据集; 二是Merchant等(
2023 Nature 624 80 )通过深度学习和图神经网络方法发现的26305种晶体结构, 构成NED弹性数据集. 最终, 本研究预测了80664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https://doi.org/10.57760/sciencedb.j00213.00104 中访问获取.Inorganic crystal materials have shown extensive application potential in many fields due to their excellent physical and chemical properties. 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 efficiency. With the development of computational methods, theoretical simulation has gradually become an effective alternative to experiments. In recent years, graph neural network-based machine learning methods have achieved remarkable results in predicting the elastic properties of inorganic crystal materials, especially, crystal graph convolutional neural networks (CGCNNs), which perform well in the prediction and expansion of material data. In this study, two CGCNN models are trained by using the shear modulus and bulk modulus data of 10987 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 coefficient of determination ($ R^2$) is close to 1. Then, two datasets are screened for materials with a band gap between 0.1 and 3.0 eV and the compounds containing radioactive elements are excluded. The dataset consists of two parts: the first part is composed of 54359 crystal structures selected from the Materials Project database, which constitute the MPED dataset; the second part is the 26305 crystal structures discovered by Merchant et al. ( 2023 Nature 624 80 ) through deep learning and graph neural network methods, which constitute the NED dataset. Finally, the shear modulus and bulk modulus of 80664 inorganic crystals are predicted in this study This work enriches the existing material elastic data resources and provides more data support for material design. All the data presented in this paper are openly available athttps://doi.org/10.57760/sciencedb.j00213.00104 .-
Keywords:
- inorganic crystal materials /
- elastic modulus /
- machine learning /
- data prediction
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ID-number Formula N ρ V M B G $ \upsilon_{\rm{l}} $ $ \upsilon_{\rm{t}} $ $ \upsilon_{\rm{s}} $ ν $ \theta_{\rm{D}} $ mp-1000 BaTe 2 4.938 89.094 264.927 31.764 23.469 2180.121 3573.541 2407.744 0.204 160.498 mp-10009 GaTe 8 5.1549 254.251 789.292 24.095 16.757 1802.955 3001.379 1994.276 0.218 93.722 mp-1001012 Sc2ZnSe4 14 3.254 289.440 567.162 53.623 32.397 3155.374 5454.806 3502.473 0.249 157.640 mp-1001015 Y2ZnS4 14 3.675 335.691 742.961 60.652 25.843 2651.771 5087.157 2967.069 0.314 127.104 mp-1001016 Sc2ZnSe4 14 4.687 333.879 942.322 54.940 22.543 2193.172 4258.659 2455.876 0.320 105.395 mp-1001019 MgSc2Se4 14 4.086 349.578 860.114 52.875 22.985 2371.850 4521.352 2652.741 0.310 112.113 mp-1001021 Y2ZnSe4 14 4.811 385.950 1118.121 55.070 22.939 2183.662 4219.640 2444.462 0.317 99.958 mp-1001023 BeC2 6 1.879 58.402 66.067 132.395 102.494 7386.608 11967.830 8148.016 0.192 625.248 mp-1001024 Y2MgS4 14 3.173 345.765 660.753 56.994 26.037 2864.435 5375.943 3200.229 0.302 135.747 mp-1001034 MgIn2Se4 14 5.031 376.146 1139.562 39.515 21.476 2066.136 3680.578 2299.251 0.270 94.830 mp-1001069 Li48P16S61 125 1.743 2652.952 2784.713 19.812 7.267 2041.845 4114.028 2291.557 0.337 49.283 mp-1001079 LiC2N2 10 1.505 130.116 117.952 56.823 20.405 3681.742 7471.454 4133.696 0.340 242.869 mp-10013 SnS 2 3.596 69.620 150.775 17.613 5.617 1249.772 2642.016 1406.249 0.356 101.772 mp-1001594 C4O3 84 1.656 1155.735 1152.492 19.101 12.904 2791.530 4682.464 3090.023 0.224 87.663 mp-1001604 LuTlS2 4 7.377 99.825 443.480 49.490 20.396 1662.754 3224.127 1861.754 0.319 119.486 mp-1001611 LuTlSe2 4 8.001 111.508 537.270 43.737 22.793 1687.844 3043.848 1880.122 0.278 116.295 mp-1001780 LuCuS2 4 6.522 77.056 302.643 74.239 35.316 2327.021 4313.132 2597.493 0.295 181.731 mp-1001786 LiScS2 4 2.700 71.362 116.027 58.972 36.372 3670.409 6309.130 4072.100 0.244 292.285 mp-1001790 LiO3 4 2.130 42.828 54.939 46.463 28.415 3652.317 6292.720 4052.874 0.246 344.878 mp-1001831 LiB 4 2.099 28.090 35.504 111.075 134.490 8004.910 11762.661 8727.079 0.069 854.731 
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Filename N ρ V M G B $ \upsilon_\mathrm{l} $ $ \upsilon_\mathrm{t} $ $ \upsilon_\mathrm{s} $ ν $ \theta_\mathrm{D} $ FIrS 3 7.798 51.805 243.280 28.413 54.027 3433.128 1908.824 2125.825 0.276 244.862 AuGeP 3 7.381 67.619 300.580 23.064 55.970 3427.627 1767.655 1979.213 0.319 208.603 GdHO 3 7.384 39.190 174.257 62.945 113.409 5169.778 2919.774 3247.588 0.266 410.537 LiPrPtSn 4 9.285 82.565 461.643 31.112 78.216 3590.578 1830.554 2051.127 0.324 222.617 ErLiPdSn 4 8.792 75.424 399.330 36.874 81.235 3851.257 2047.962 2288.361 0.303 255.968 BaBiHgNa 4 6.817 138.827 569.887 11.187 24.989 2419.500 1281.048 1431.855 0.305 130.688 BeGeHLa 4 5.801 63.421 221.566 49.688 90.981 5206.069 2926.621 3256.448 0.269 385.920 AlHKSb 4 3.004 104.402 188.848 14.352 23.461 3765.877 2185.915 2425.631 0.246 243.454 EuHgNaSb 4 7.135 115.739 497.304 15.654 30.762 2690.122 1481.228 1650.873 0.282 160.097 LiNiSmSn 4 7.617 72.963 334.704 36.441 70.798 3958.873 2187.199 2437.061 0.280 275.632 DyLiPdSn 4 8.557 76.573 394.571 35.786 81.074 3879.627 2045.067 2286.509 0.308 254.475 N2SSe2 5 2.175 166.436 217.998 2.459 2.521 1632.981 1063.352 1165.878 0.132 107.904 LiNaSe2Zn 5 3.916 107.396 253.260 17.754 31.924 3767.961 2129.286 2368.236 0.265 253.647 BrGeLa2Rh 5 6.436 137.585 533.260 27.302 50.532 3675.249 2059.620 2292.318 0.271 226.057 CsHgNaS2 5 4.774 146.289 420.615 9.852 18.449 2572.057 1436.510 1599.245 0.273 154.518 AlAs2CsMg 5 3.863 143.606 334.035 20.025 28.181 3769.434 2276.944 2517.185 0.213 244.713 Br2GeSmY 5 4.803 163.091 471.714 19.469 32.496 3488.675 2013.383 2235.315 0.250 208.287 As2Ca2Sr 5 3.392 155.481 317.619 28.093 37.392 4697.360 2877.774 3176.871 0.200 300.774 KLiMnTe2 5 3.860 153.218 356.177 12.890 26.438 3361.705 1827.331 2038.601 0.290 193.953 Al2C2Yb 5 6.426 64.862 251.024 88.642 125.838 6162.150 3713.927 4106.697 0.215 520.350
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