AI 中文总结
研究旨在构建自旋轨道耦合万尼尔哈密顿量通用模型，核心方法是引入G(Wa)NN模型并结合优化方法，主要贡献为实现大规模输运模拟、支持局部微调，通过Tailwater包将预测转化为可观测量，弥合相关差距。
AI 中文摘要
虽然机器学习原子间势已成熟到可变革材料科学，但电子结构的深度学习模型才刚刚出现，且几乎仅限于非正交基哈密顿量。我们引入了G(Wa)NN，这是首个能在正交万尼尔基中生成固态系统电子哈密顿量的深度学习模型。它在超过111K个万尼尔哈密顿量的多样数据集上训练。优化的推理和线性缩放方法相结合实现大规模输运模拟，该框架支持局部微调。还引入了Tailwater包，能将预测转化为物理可观测量，其生态系统旨在弥合深度学习与宏观量子输运模拟的差距。
英文摘要
While machine learning interatomic potentials (MLiPs) have matured to revolutionize material science, deep learning models for electronic structure are just beginning to emerge and restricted, almost exclusively, to non-orthogonal basis Hamiltonians. We introduce G(Wa)NN, the first deep-learning model capable of generating the electronic Hamiltonian of solid-state systems in an orthogonal Wannier basis. G(Wa)NN is trained on an unprecedented, diverse dataset of more than 111K Wannier Hamiltonians (150M+ hopping matrices) spanning 69 elements. The combination of optimized inference and linear-scaling methods for orthogonal Hamiltonians unlock transport simulations at massive scales (10K+ atoms). Crucially, the framework supports local finetuning, allowing users to adapt the base model to custom Wannier Hamiltonian datasets. To seamlessly translate these predictions into physical observables, we introduce Tailwater, a Python package providing an API interface to G(Wa)NN alongside a high performance post-processing library. Tailwater enables automated projection of the predicted Hamiltonian into an arbitrary low-energy subspace-directly mirroring familiar Wannier90 workflows-and includes a suite of Kernel Polynomial Method (KPM) functions that exploit the orthogonal basis to achieve strict linear scaling for spectral observables. The Tailwater ecosystem, with the G(Wa)NN model at its core, aims to help bridge the gap between deep learning and macro-scale quantum transport simulations.
