Kumar Shashikant, Jing Xin, Pask John E, Medford Andrew J, Suryanarayana Phanish
College of Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
J Chem Phys. 2023 Dec 28;159(24). doi: 10.1063/5.0180541.
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.
我们提出了一种用于从无轨道密度泛函理论（DFT）计算中获得Kohn-Sham精度的Δ机器学习模型。具体而言，我们采用基于核方法的机器学习力场（MLFF）方案来捕捉Kohn-Sham和无轨道DFT能量/力之间的差异。我们在实时分子动力学模拟的背景下实现了该模型，并研究了其对代表性系统的准确性、性能和参数敏感性。我们发现，该形式体系不仅将Thomas-Fermi-von Weizsäcker无轨道能量和力的精度提高了两个多数量级，而且比仅基于Kohn-Sham DFT的MLFF更准确，同时效率更高且对模型参数更不敏感。我们应用该框架研究了熔融Al0.88Si0.12的结构，结果表明Si原子没有聚集，这与之前在小一个数量级的长度和时间尺度上进行的Kohn-Sham研究一致。
