标题： 基于深度学习的材料晶体结构形成能预测 显示英文标题

标题： Formation Energy Prediction of Material Crystal Structures using Deep Learning

摘要： 确定化学化合物的稳定性对于推进材料发现至关重要。 在本研究中，我们引入了一种新的深度神经网络模型，旨在预测晶体的形成能，从而确定其稳定性特性。 我们的模型利用来自材料组成的元素分数，并将对称性分类作为额外的输入特征。 材料的对称性分类代表了晶体多形性，并对于理解材料中的相变至关重要。 我们的研究结果表明，将晶体系统、点群或空间群对称性信息整合到所开发的深度学习架构中显著提高了预测性能，其中当整合空间群分类时达到了最高准确率。 此外，我们使用相同的模型架构来预测高于谷底的能量，这是材料稳定性的指标，并以形成能作为额外的输入特征。 显示英文摘要

摘要： Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability property. Our model leverages elemental fractions derived from material composition and incorporates the symmetry classification as an additional input feature. The materials' symmetry classifications represent the crystal polymorphs and are crucial for understanding phase transitions in materials. Our findings demonstrate that the integration of crystal system, point group, or space group symmetry information significantly enhances the predictive performance of the developed deep learning architecture, where the highest accuracy was achieved when space group classification was incorporated. In addition, we use the same model architecture to predict the energy above hull, an indicator to material stability, with formation energy as an additional input feature.