基于图神经网络的边张量预测
Edge-based Tensor prediction via graph neural networks
摘要：
消息传递神经网络（MPNN）在预测分子和晶体的物理性质方面表现出极高的效率和精度，有望成为继密度泛函理论（DFT）之后的下一代材料模拟工具。然而，目前缺乏一个通用的MPNN框架，直接预测的张量性质的晶体。在这项工作中，张量性质的预测提出了一个通用的框架：一个晶体的张量性质可以分解为平均的所有原子的张量贡献的晶体，每个原子的张量贡献可以扩展为张量投影的总和在连接原子的边缘方向。在此基础上，提出了力矢量、玻恩有效电荷张量、介电张量和压电张量的边展开式。这些展开式是旋转等变的，而这些张量展开式中的系数是旋转不变标量，其类似于物理量，诸如形成能和带隙。这种张量预测框架的优点是它不需要网络本身是等变的。因此，在本工作中，我们直接在不变图神经网络的基础上设计了基于边的张量预测图神经网络（ETGNN）模型来预测张量。在扩展系统、随机扰动结构和JARVIS-DFT数据集上的测试结果表明了该张量预测框架的有效性和高精度。这种张量预测框架对于几乎所有的GNN都是通用的，并且可以在未来使用更先进的GNN实现更高的准确性。
英文摘要：
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT). However, there is currently a lack of a general MPNN framework for directly predicting the tensor properties of the crystals. In this work, a general framework for the prediction of tensor properties was proposed: the tensor property of a crystal can be decomposed into the average of the tensor contributions of all the atoms in the crystal, and the tensor contribution of each atom can be expanded as the sum of the tensor projections in the directions of the edges connecting the atoms. On this basis, the edge-based expansions of force vectors, Born effective charges (BECs), dielectric (DL) and piezoelectric (PZ) tensors were proposed. These expansions are rotationally equivariant, while the coefficients in these tensor expansions are rotationally invariant scalars which are similar to physical quantities such as formation energy and band gap. The advantage of this tensor prediction framework is that it does not require the network itself to be equivariant. Therefore, in this work, we directly designed the edge-based tensor prediction graph neural network (ETGNN) model on the basis of the invariant graph neural network to predict tensors. The validity and high precision of this tensor prediction framework were shown by the tests of ETGNN on the extended systems, random perturbed structures and JARVIS-DFT datasets. This tensor prediction framework is general for nearly all the GNNs and can achieve higher accuracy with more advanced GNNs in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
