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For orbital-free {\it ab initio} molecular dynamics, especially on systems in extreme thermodynamic conditions, we provide the first pseudo-potential-adapted generalized gradient approximation (GGA) functional for the non-interacting free…

Chemical Physics · Physics 2020-02-19 Kai Luo , Valentin V. Karasiev , S. B. Trickey

We report the first wholly non-empirical generalized gradient approximation, non-interacting free energy functional for orbital-free density functional theory and use that new functional to provide forces for finite-temperature molecular…

Materials Science · Physics 2013-11-26 Valentin V. Karasiev , Debajit Chakraborty , Olga A. Shukruto , S. B. Trickey

We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using…

We have developed and implemented a new quantum molecular dynamics approximation that allows fast and accurate simulations of dense plasmas from cold to hot conditions. The method is based on a carefully designed orbital-free implementation…

Plasma Physics · Physics 2015-06-22 Travis Sjostrom , Jerome Daligault

Simulations in the warm dense matter regime using finite temperature Kohn-Sham density functional theory (FT-KS-DFT), while frequently used, are computationally expensive due to the partial occupation of a very large number of high-energy…

Materials Science · Physics 2018-04-04 Yael Cytter , Eran Rabani , Daniel Neuhauser , Roi Baer

We present a real-space formulation and higher-order finite-difference implementation of periodic Orbital-free Density Functional Theory (OF-DFT). Specifically, utilizing a local reformulation of the electrostatic and kernel terms, we…

Computational Physics · Physics 2015-12-23 Swarnava Ghosh , Phanish Suryanarayana

Orbital-free density functional theory (OF-DFT) is a promising method for large-scale quantum mechanics simulation as it provides a good balance of accuracy and computational cost. Its applicability to large-scale simulations has been aided…

Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been…

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible…

Simulation of warm dense matter requires computational methods that capture both quantum and classical behavior efficiently under high-temperature, high-density conditions. Currently, density functional theory molecular dynamics is used to…

Chemical Physics · Physics 2015-10-21 Attila Cangi , Aurora Pribram-Jones

Understanding many processes, e.g. fusion experiments, planetary interiors and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter (WDM) and hot dense plasma. This complex state of matter consists of a…

Computational Physics · Physics 2020-08-05 Alexander J. White , Lee A. Collins

We present a $\Delta$-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…

Chemical Physics · Physics 2023-10-11 Shashikant Kumar , Xin Jing , John E. Pask , Andrew J. Medford , Phanish Suryanarayana

Million-atom quantum simulations are in principle feasible with Orbital-Free Density Functional Theory (OF-DFT) because the algorithms only require simple functional minimizations with respect to the electron density function. In this…

Materials Science · Physics 2019-07-17 Wenhui Mi , Michele Pavanello

F.E. Harris has been a significant partner in our work on orbital-free density functional approximations for use in ab initio molecular dynamics. Here we mention briefly the essential progress on single-point functionals since our original…

Chemical Physics · Physics 2016-01-12 Valentin V. Karasiev , S. B. Trickey

Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance…

Computational Physics · Physics 2023-04-04 Chuin Wei Tan , Chris J. Pickard , William C. Witt

Orbital-free Density Functional Theory (OF-DFT) has been used when studying atoms, molecules and solids. In nuclear physics, there has been basically no application of OF-DFT so far, as the Density Functional Theory (DFT) has been widely…

Nuclear Theory · Physics 2023-08-03 Gianluca Colo' , Kouichi Hagino

Development of the electronic kinetic-energy density functional is a subject of major interest in theoretical physics and chemistry. In this work, the nonlocal kinetic-energy functional is developed in terms of the response function for the…

Chemical Physics · Physics 2022-06-17 Hideaki Takahashi

Time-dependent orbital-free DFT is an efficient method for calculating the dynamic properties of large scale quantum systems due to the low computational cost compared to standard time-dependent DFT. We formalize this method by mapping the…

Chemical Physics · Physics 2021-06-09 Kaili Jiang , Michele Pavanello

Finite-temperature orbital-free density functional theory (FT-OFDFT) holds significant promise for simulating warm dense matter due to its favorable scaling with both system size and temperature. However, the lack of the numerically…

Computational Physics · Physics 2024-12-04 Cheng Ma , Min Chen , Yu Xie , Qiang Xu , Wenhui Mi , Yanchao Wang , Yanming Ma

Molecular Density Functional Theory (MDFT) offers an efficient implicit- solvent method to estimate molecule solvation free-energies whereas conserving a fully molecular representation of the solvent. Even within a second order ap-…

Soft Condensed Matter · Physics 2014-06-12 Volodymyr P. Sergiievskyi , Guillaume Jeanmairet , Maximilien Levesque , Daniel Borgis
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