Riemelmoser Stefan, Verdi Carla, Kaltak Merzuk, Kresse Georg
Faculty of Physics and Center for Computational Materials Science, University of Vienna, Kolingasse 14-16, A-1090 Vienna, Austria.
Vienna Doctoral School in Physics, University of Vienna, Boltzmanngasse 5, A-1090 Vienna, Austria.
J Chem Theory Comput. 2023 Oct 24;19(20):7287-7299. doi: 10.1021/acs.jctc.3c00848. Epub 2023 Oct 6.
Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we use machine learning to map the RPA to a pure Kohn-Sham density functional. The machine learned RPA model (ML-RPA) is a nonlocal extension of the standard gradient approximation. The density descriptors used as ingredients for the enhancement factor are nonlocal counterparts of the local density and its gradient. Rather than fitting only RPA exchange-correlation energies, we also include derivative information in the form of RPA optimized effective potentials. We train a single ML-RPA functional for diamond, its surfaces, and liquid water. The accuracy of ML-RPA for the formation energies of 28 diamond surfaces reaches that of state-of-the-art van der Waals functionals. For liquid water, however, ML-RPA cannot yet improve upon the standard gradient approximation. Overall, our work demonstrates how machine learning can extend the applicability of the RPA to larger system sizes, time scales, and chemical spaces.
科恩-沈密度泛函理论（DFT）是计算化学和材料科学中进行第一性原理计算的标准方法。诸如随机相位近似（RPA）等更精确的理论，由于其巨大的计算成本，应用受到限制。在此，我们利用机器学习将RPA映射到一个纯科恩-沈密度泛函。机器学习的RPA模型（ML-RPA）是标准梯度近似的非局部扩展。用作增强因子成分的密度描述符是局部密度及其梯度的非局部对应物。我们不仅拟合RPA交换关联能，还以RPA优化有效势的形式纳入导数信息。我们针对金刚石、其表面以及液态水训练了一个单一的ML-RPA泛函。ML-RPA对28个金刚石表面形成能的计算精度达到了最先进的范德华泛函的精度。然而，对于液态水，ML-RPA尚未能在标准梯度近似的基础上有所改进。总体而言，我们的工作展示了机器学习如何能够将RPA的适用性扩展到更大的系统规模、时间尺度和化学空间。
