Mi Wenhui, Luo Kai, Trickey S B, Pavanello Michele
Key Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, PR China.
State Key Laboratory of Superhard Materials, Jilin University, Changchun 130012, PR China.
Chem Rev. 2023 Nov 8;123(21):12039-12104. doi: 10.1021/acs.chemrev.2c00758. Epub 2023 Oct 23.
Kohn-Sham Density Functional Theory (KSDFT) is the most widely used electronic structure method in chemistry, physics, and materials science, with thousands of calculations cited annually. This ubiquity is rooted in the favorable accuracy vs cost balance of KSDFT. Nonetheless, the ambitions and expectations of researchers for use of KSDFT in predictive simulations of large, complicated molecular systems are confronted with an intrinsic computational cost-scaling challenge. Particularly evident in the context of first-principles molecular dynamics, the challenge is the high cost-scaling associated with the computation of the Kohn-Sham orbitals. Orbital-free DFT (OFDFT), as the name suggests, circumvents entirely the explicit use of those orbitals. Without them, the structural and algorithmic complexity of KSDFT simplifies dramatically and near-linear scaling with system size irrespective of system state is achievable. Thus, much larger system sizes and longer simulation time scales (compared to conventional KSDFT) become accessible; hence, new chemical phenomena and new materials can be explored. In this review, we introduce the historical contexts of OFDFT, its theoretical basis, and the challenge of realizing its promise via approximate kinetic energy density functionals (KEDFs). We review recent progress on that challenge for an array of KEDFs, such as one-point, two-point, and machine-learnt, as well as some less explored forms. We emphasize use of exact constraints and the inevitability of design choices. Then, we survey the associated numerical techniques and implemented algorithms specific to OFDFT. We conclude with an illustrative sample of applications to showcase the power of OFDFT in materials science, chemistry, and physics.
科恩-沈密度泛函理论(KSDFT)是化学、物理和材料科学中使用最广泛的电子结构方法,每年都有成千上万次计算被引用。这种广泛应用源于KSDFT在精度与成本之间的良好平衡。尽管如此,研究人员在使用KSDFT对大型复杂分子系统进行预测性模拟时的雄心和期望,却面临着固有的计算成本缩放挑战。在第一性原理分子动力学的背景下,这种挑战尤为明显,即与科恩-沈轨道计算相关的高成本缩放。无轨道密度泛函理论(OFDFT),顾名思义,完全避免了对这些轨道的显式使用。没有这些轨道,KSDFT的结构和算法复杂性会大幅简化,并且无论系统状态如何,都可以实现与系统大小近乎线性的缩放。因此,可以处理更大的系统规模和更长的模拟时间尺度(与传统KSDFT相比);从而可以探索新的化学现象和新材料。在这篇综述中,我们介绍了OFDFT的历史背景、其理论基础,以及通过近似动能密度泛函(KEDF)实现其前景所面临的挑战。我们回顾了针对一系列KEDF(如单点、两点和机器学习的KEDF)以及一些较少探索的形式在该挑战上的最新进展。我们强调精确约束的使用以及设计选择的必然性。然后,我们调查了与OFDFT相关的数值技术和已实现的算法。我们以一个说明性的应用示例作为结尾,以展示OFDFT在材料科学、化学和物理中的强大功能。