论文标题
可转移的E（3）分子和固体的哈密顿量参数化
Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
论文作者
论文摘要
在机器学习（ML）中使用消息通信机制，而不是自洽的迭代，直接构建从结构到电子汉密尔顿矩阵的映射将大大提高密度功能理论（DFT）计算的效率。在这项工作中，我们提出了E（3）e象框架中的一般分析性汉密尔顿表示，该框架可以通过完整的数据驱动方法符合分子和固体的初始算法，并且在旋转，空间反转和时间逆转操作下是均等的。我们的模型在基准测试中达到了最先进的精度，并准确地预测了各种周期和上周期系统的电子汉密尔顿矩阵和相关特性，显示出很高的可传递性和概括能力。该框架提供了一个通用的可转移模型，该模型可用于加速具有相同网络权重的不同大型系统上的电子结构计算，该系统具有相同的小型结构训练的网络权重。
Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.
