密度泛函理论
形式主义(音乐)
Kohn-Sham方程
核(代数)
轨道自由密度泛函理论
物理
力场(虚构)
背景(考古学)
含时密度泛函理论
统计物理学
计算机科学
量子力学
数学
古生物学
视觉艺术
艺术
音乐剧
组合数学
生物
作者
Shashikant Kumar,Xin Jing,John E. Pask,Andrew J. Medford,Phanish Suryanarayana
摘要
We present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn–Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas–Fermi–von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn–Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al0.88Si0.12, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn–Sham study performed at an order of magnitude smaller length and time scales.
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