Literature DB >> 29764156

Nonlocal kinetic energy functionals by functional integration.

Wenhui Mi1, Alessandro Genova1, Michele Pavanello1.   

Abstract

Since the seminal studies of Thomas and Fermi, researchers in the Density-Functional Theory (DFT) community are searching for accurate electron density functionals. Arguably, the toughest functional to approximate is the noninteracting kinetic energy, Ts[ρ], the subject of this work. The typical paradigm is to first approximate the energy functional and then take its functional derivative, δTs[ρ]δρ(r), yielding a potential that can be used in orbital-free DFT or subsystem DFT simulations. Here, this paradigm is challenged by constructing the potential from the second-functional derivative via functional integration. A new nonlocal functional for Ts[ρ] is prescribed [which we dub Mi-Genova-Pavanello (MGP)] having a density independent kernel. MGP is constructed to satisfy three exact conditions: (1) a nonzero "Kinetic electron" arising from a nonzero exchange hole; (2) the second functional derivative must reduce to the inverse Lindhard function in the limit of homogenous densities; (3) the potential is derived from functional integration of the second functional derivative. Pilot calculations show that MGP is capable of reproducing accurate equilibrium volumes, bulk moduli, total energy, and electron densities for metallic (body-centered cubic, face-centered cubic) and semiconducting (crystal diamond) phases of silicon as well as of III-V semiconductors. The MGP functional is found to be numerically stable typically reaching self-consistency within 12 iterations of a truncated Newton minimization algorithm. MGP's computational cost and memory requirements are low and comparable to the Wang-Teter nonlocal functional or any generalized gradient approximation functional.

Entities:  

Year:  2018        PMID: 29764156     DOI: 10.1063/1.5023926

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  4 in total

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Authors:  William C Witt; Benjamin W B Shires; Chuin Wei Tan; Wojciech J Jankowski; Chris J Pickard
Journal:  J Phys Chem A       Date:  2021-02-15       Impact factor: 2.781

2.  Nonlocal pseudopotential energy density functional for orbital-free density functional theory.

Authors:  Qiang Xu; Cheng Ma; Wenhui Mi; Yanchao Wang; Yanming Ma
Journal:  Nat Commun       Date:  2022-03-16       Impact factor: 14.919

3.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

4.  Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative.

Authors:  Ralf Meyer; Manuel Weichselbaum; Andreas W Hauser
Journal:  J Chem Theory Comput       Date:  2020-08-25       Impact factor: 6.006

  4 in total

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