完整神经电子初始化加速材料DFT
Complete Neural Electronic Initialization Accelerates Materials DFT
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中文总结 AI 辅助
提出首个满足七项标准的完整神经电子初始化器，通过AugNet预测缀加占据数和自旋密度，实现无参考PAW DFT加速，端到端时间减少约25%。
中文摘要 AI 辅助
我们提出了首个用于在投影缀加波（PAW）形式下加速材料平面波密度泛函理论（DFT）的完整机器学习方法。我们形式化了七个标准，这些标准是\u201c完整神经电子初始化器\u201d在实际端到端PAW DFT加速中必须满足的。将这些标准应用于先前的工作，揭示了两个缺失的结构依赖组件：缀加占据数和自旋初始化，这些缺失使得现有方法无法提供完整的无参考初始化。受控消融实验表明，省略这些组件可能会消除或逆转仅预测平滑价电子密度的模型所获得的加速效果。我们通过引入AugNet来满足这些缺失的需求，AugNet是首个用于PAW缀加占据数的通用等变模型，也是首个用于材料的通用自旋密度模型，该模型预测平滑自旋差密度和自旋差PAW缀加占据数，并使用预测的磁矩来约束全局磁态。与现有的价电子密度模型相结合，这些组件满足了所有七个标准，并形成了一个完全无参考的材料DFT电子初始化器，无需来自收敛目标计算的任何电子量。我们的方法在未见过的结构上将端到端DFT墙钟时间减少了高达约25%，同时保持了收敛的能量。
英文摘要
We present the first complete machine learning method for accelerating plane-wave density functional theory (DFT) in materials under the projector augmented wave (PAW) formalism. We formalize seven criteria that a Complete Neural Electronic Initializer must satisfy for practical end-to-end PAW DFT acceleration. Applying these to prior work reveals two structure-dependent components, augmentation occupancies and spin initialization, whose absence prevents existing acceleration methods from providing complete reference-free initialization. We show that omitting these components can eliminate or reverse the acceleration obtained via models that only predict the smooth valence density. We satisfy the missing requirements by introducing AugNet, a general equivariant model for PAW augmentation occupancies, and the first general spin density model for materials, which predicts the smooth spin-difference density and spin-difference PAW augmentation occupancies using predicted magnetic moments to constrain the global magnetic state. Combined with existing valence density models, our full method satisfies all seven criteria and forms a fully reference-free electronic initializer for materials DFT, requiring no electronic quantities from a converged target calculation. We show that perfect initialization could cut PAW DFT wall time by 40-52%, and our method recovers up to 62% of this saving, reducing end-to-end DFT wall time by up to ~25% on unseen structures while preserving converged energies.
发表机构
- Technical University of Denmark（丹麦技术大学）
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