【摘要】 晶体学空间群是描述晶体结构的一个重要特征,但仅在给定的化学成分下很难确定晶体的空间群.本文提出了一种深度学习方法,从化学式中预测晶体结构的空间群.建立了包含34528个稳定化合物的数据集,其中72%的数据集被用作训练集,8%的数据集被用作验证集,20%的数据集被用作测试集.基于深度学习的结果,本文提出了一个模型,该模型在测试集前1名、前5名和前10名的预测结果中,获得真实晶体学空间群准确率分别为60.8%、76.5%和82.6%.比较验证集和测试集的预测结果,深度学习模型表现出良好的泛化能力.此外,230个晶体组被分为19个新的标签,包括18个代表性强的晶体学空间群,每个空间群包含400多个化合物,以及山其他212个空间群中剩余化合物组成的一个标签.在19个新标签上训练的深度学习模型在识别晶体学空间群方面取得了较好的结果,预测准确率为72.2%.提供了一种有效的深度学习模型,能够仅从化学成分上识别晶体结构的晶体学空间群.更多还原

【Abstract】 Crystallographic group is an important character to describe the crystal structure, but it is difficult to identify the crystallographic group of crystal when only chemical composition is given. Here,we present a machine-learning method to predict the crystallographic group of crystal structure from its chemical formula. 34528 stable compounds in 230 crystallographic groups are investigated, of which 72% of data set are used as training set, 8% as validation set, and 20% as test set. Based on the results of machine learning, we present a model which can obtain correct crystallographic group in the top-1, top-5, and top-10 results with the estimated accuracy of 60.8%, 76.5%, and 82.6%, respectively. In particular, the performance of deep-learning model presents high generalization through comparison between validation set and test set. Additionally, 230 crystallographic groups are classified into 19new labels, denoting 18 heavily represented crystallographic groups with each containing more than 400 compounds and one combination group of remaining compounds in other 212crystallographic groups. A deep-learning model trained on 19 new labels yields a promising result to identify crystallographic group with the estimated accuracy of 72.2%. Our results provide a promising approach to identify crystallographic group of crystal structures only from their chemical composition.更多还原