面向晶体结构预测的深度学习方法

晶体结构研究是研究固体材料物理化学性质的基础,而筛选晶体结构通常基于能量最低原理,采用密度泛函理论计算结构能量需要大量计算资源及服务时间.为此本文提出了面向材料结构预测的深度学习方法,加快材料晶体结构的预测.本文从数据集优化、模型训练策略、算法优化等方面进行了深入研究,确定了应用于材料结构预测中深度学习的网络参数和优化算法.将确定的深度学习框架用于寻找Si单晶、TiO2和CaTiO3化合物的基态稳定结构,实验结果表明,利用本研究提出的深度学习方法预测的晶体结构与实验室制备材料结构相吻合.

Deep Learning Method for Crystal Structure Prediction

The study of crystal structure is the basis for studying the physical and chemical properties of solid materials, and the screening of crystal structure is usually based on the principle of least energy. The use of density functional theory to calculate the structure energy requires a lot of computing resources and service time. For this reason, this research proposes a deep learning method for material structure prediction to speed up the prediction of material crystal structure. This work systematically studied and analyzed the data set optimization, training method, algorithm optimization, and so on. The network parameters and optimized algorithm of deep learning for crystal structure prediction are confirmed and coded. The optimized deep learning method is used to find out stable structure of Silicon, titanium dioxide, and perovskite CaTiO₃, the predicted structures are well agreement with the experimental results.