论文标题
在深度学习模型中利用轨道信息和原子特征
Leveraging Orbital Information and Atomic Feature in Deep Learning Model
论文作者
论文摘要
长期以来，预测材料微结构的材料特性一直是一个具有挑战性的问题。最近，已经为材料财产预测开发了许多深度学习方法。在这项研究中，我们提出了一个晶体表示学习框架，Orbital Crystalnet，Ocrystalnet，该框架由两个部分组成：原子描述符的产生和图形表示学习。在Ocrystalnet中，我们首先将轨道场矩阵（OFM）和原子特征掺入M-Feature原子描述符的构建体，然后将原子描述符用作原子键消息传递模块中的原子嵌入，以利用水晶图的拓扑结构来学习水晶表达。为了证明Ocrystalnet的功能，我们在材料项目数据集和Jarvis数据集上执行了许多预测任务，并将我们的模型与其他基线和最先进的方法进行了比较。为了进一步介绍Ocrystalnet的有效性，我们对模型进行了消融研究和案例研究。结果表明，我们的模型比其他最先进的模型具有各种优势。
Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal representation learning framework, Orbital CrystalNet, OCrystalNet, which consists of two parts: atomic descriptor generation and graph representation learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and atomic features to construct OFM-feature atomic descriptor, and then the atomic descriptor is used as atom embedding in the atom-bond message passing module which takes advantage of the topological structure of crystal graphs to learn crystal representation. To demonstrate the capabilities of OCrystalNet we performed a number of prediction tasks on Material Project dataset and JARVIS dataset and compared our model with other baselines and state of art methods. To further present the effectiveness of OCrystalNet, we conducted ablation study and case study of our model. The results show that our model have various advantages over other state of art models.
