|
|
机器学习在材料性质预测中的运用
|
Abstract:
本文采用机器学习算法对晶体的生成焓的预测进行了研究。利用由多个神经层组成的深度神经网络(DNN)模型学习数据间的关系，对深度学习模型在材料结生成焓的预测做了深入的研究和讨论。通过对开放量子材料数据库(OQMD)中275,778种化合物的生成焓参数进行学习，建立了深度学习多层全连接网络，并用来预测未知晶体材料的生成焓。优化后预测模型的精度达到了0.075 eV/atom，达到了量子力学软件的计算精度。
The prediction of enthalpy of formation of the crystal is studied by the machine learning algo-rithm. Based on the relationship between the learning data of the deep neural network (DNN) model composed of multiple neural layers, the prediction of enthalpy of formation of the deep learning model in the material junction is studied and discussed in depth. By learning the en-thalpy of formation parameters of 275,778 compounds in the open quantum materials database (OQMD), a deep learning multi-layer fully connected network was established to predict the en-thalpy of formation of unknown crystal materials. The accuracy of the optimized prediction model reached 0.075 eV/atom, which reached the computational accuracy of the quantum me-chanics software.
| [1] | Curtarolo, S., Kolmogorov, A.N. and Cocks, F.H. (2005) High-Throughput ab Initio Analysis of the Bi-In, Bi-Mg, Bi-Sb, In-Mg, In-Sb, and Mg-Sb systems. Calphad, 29, 155-161. https://doi.org/10.1016/j.calphad.2005.04.003 |
| [2] | Oganov, A.R. and Glass, C.W. (2006) Crystal Structure Prediction Using ab Initio Evolutionary Techniques: Principles and Applications. The Journal of Chemical Physics, 124, Article ID: 244704. https://doi.org/10.1063/1.2210932 |
| [3] | Hautier, G., Jain, A., Mueller, T., et al. (2013) Designing Multielectron Lithium-Ion Phosphate Cathodes by Mixing Transition Metals. Chemistry of Materials, 25, 2064-2074. https://doi.org/10.1021/cm400199j |
| [4] | Rupp, M., Tkatchenko, A., Müller, K.R., et al. (2012) Reply to Comment on Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Physical Review Letters, 109, Article ID: 059802.
https://doi.org/10.1103/PhysRevLett.108.058301 |
| [5] | Snyder, J.C., Rupp, M., Hansen, K., et al. (2012) Finding Density Functionals with Machine Learning. Physical Review Letters, 108, Article ID: 253002. https://doi.org/10.1103/PhysRevLett.108.253002 |
| [6] | Wolverton, C., Yan, X.Y., Vijayaraghavan, R., et al. (2002) Incorporating First-Principles Energetics in Computational Thermodynamics Approaches. Acta Materialia, 50, 2187-2197. https://doi.org/10.1016/S1359-6454(01)00430-X |
| [7] | Ceder, G. (1998) Predicting Properties from Scratch. Science, 280, 1099-1100.
https://doi.org/10.1126/science.280.5366.1099 |
| [8] | Moreels, P. and Perona, P. (2007) Evaluation of Features Detectors and Descriptors Based on 3D Objects. International Journal of Computer Vision, 73, 263-284. https://doi.org/10.1007/s11263-006-9967-1 |
| [9] | Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems, Springer, New York, 1097-1105. |
| [10] | Deng, L., Li, J., Huang, J.T., et al. (2013) Recent Advances in Deep Learning for Speech Research at Microsoft. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, 26-31 May 2013, 8604-8608.
https://doi.org/10.1109/ICASSP.2013.6639345 |
| [11] | Braun, M.L., Buhmann, J.M. and M？？ller, K.R. (2008) On Relevant Dimensions in Kernel Feature Spaces. Journal of Machine Learning Research, 9, 1875-1908. |
| [12] | Geman, S., Bienenstock, E. and Doursat, R. (1992) Neural Networks and the Bias/Variance Di-lemma. Neural Computation, 4, 1-58. https://doi.org/10.1162/neco.1992.4.1.1 |
| [13] | Vapnik, V. (2013) The Nature of Statistical Learning Theory. Springer Science & Business Media, New York. |
| [14] | Cuingnet, R., Rosso, C., Chupin, M., et al. (2011) Spatial Regularization of SVM for the Detection of Diffusion Alterations Associated with Stroke Outcome. Medical Image Analysis, 15, 729-737.
https://doi.org/10.1016/j.media.2011.05.007 |
| [15] | Gaonkar, B. and Davatzikos, C. (2013) Analytic Estimation of Statistical Significance Maps for Support Vector Machine Based Multi-Variate Image Analysis and Classification. NeuroImage, 78, 270-283.
https://doi.org/10.1016/j.neuroimage.2013.03.066 |
| [16] | Boser, B.E., Guyon, I.M. and Vapnik, V.N. (1992) Proceedings of the Fifth Annual Workshop on Computational Learning Theory. |
| [17] | Rosasco, L., Vito, E.D., Ca-ponnetto, A., et al. (2004) Are Loss Functions All the Same? Neural Computation, 16, 1063-1076. https://doi.org/10.1162/089976604773135104 |
| [18] | Hastie, T. and Tibshirani, R. (1998) Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, Springer, New York, 507-513. https://doi.org/10.1214/aos/1028144844 |
| [19] | Hawkins, D.M. (2004) The Problem of Overfitting. Journal of Chemical Information and Computer Sciences, 44, 1-12.
https://doi.org/10.1021/ci0342472 |
| [20] | Nair, V. and Hinton, G.E. (2010) Rectified Linear Units Improve Re-stricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, 21-24 June 2010, 807-814. |
