无轨道代理泛函产生可迁移的原子间势和电子密度
Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron Densities
浏览论文内容
中文总结 AI 辅助
本研究提出强代理泛函，从密度预测能量，实现稳定收敛，密度误差降低一个数量级，能量预测与先进MLIPs相当且泛化更优。
中文摘要 AI 辅助
无轨道密度泛函理论旨在直接从电子密度计算电子系统的能量，避免使用单电子波函数，从而为可扩展的电子结构计算提供一条途径。机器学习无轨道密度泛函最近在小有机分子上取得了有前景的结果，以亚毫哈特里精度预测能量。然而，它们在密度优化中的收敛性仍然对超参数调整和架构选择敏感。在此，我们扩展了最近引入的（弱）代理泛函框架——该框架旨在仅预测基态电子密度——使其也能产生能量，从而得到“强”代理泛函。我们发现，这些学习到的泛函在所有测试的神经网络主干上都能实现稳定收敛，与之前的OF-DFT方法相比，将相对于Kohn-Sham参考的电子密度误差降低了一个数量级。更重要的是，预测的能量与仅基于能量训练的最先进的机器学习原子间势（MLIPs）相当，同时对更大、未见过的系统表现出优越的泛化能力。
英文摘要
Orbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offering a route to scalable electronic structure calculations. Machine-learned orbital-free density functionals have recently achieved promising results on small organic molecules, predicting energies with sub-millihartree accuracy. However, their convergence in density optimization remains sensitive to hyperparameter tuning and architectural choices. Here, we extend the recently introduced (weak) surrogate functional framework - designed to predict ground-state electron densities only - to also yield their energy, resulting in "strong" surrogate functionals. We find that these learned functionals enable stable convergence across all tested neural network backbones, reducing electron density errors relative to the Kohn-Sham reference by an order of magnitude compared to previous OF-DFT methods. More importantly, the predicted energies are competitive with state-of-the-art machine-learned interatomic potentials (MLIPs) trained only on energies, while exhibiting superior generalization to larger, unseen systems.
发表机构
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心）
机构由 AI 辅助整理，请以论文原文为准。
