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WIREs Comput Mol Sci
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DFTpy: An efficient and object‐oriented platform for orbital‐free DFT simulations

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Abstract In silico materials design is hampered by the computational complexity of Kohn–Sham DFT, which scales cubically with the system size. Owing to the development of new‐generation kinetic energy density functionals (KEDFs), orbital‐free DFT (OFDFT) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open‐source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third‐party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms from PyFFTW. New‐generation, nonlocal and density‐dependent‐kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million‐atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object‐oriented, opening the door to easy implementation of new features. As an example, we present a time‐dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal clusters recovering qualitatively the time‐dependent Kohn–Sham DFT result. The Python codebase allows for easy implementation of application programming interfaces. We showcase the combination of DFTpy and ASE for molecular dynamics simulations of liquid metals. DFTpy is released under the MIT license. This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Density Functional Theory Data Science > Computer Algorithms and Programming
Timings (wall time) for density optimization on fcc aluminum for systems up to 1 million atoms with the truncated Newton method
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Timings (wall time) for density optimizations carried out with different optimization methods. (a) Comparing optimizers on bulk Al supercells. (b) Comparing two‐ and three‐step optimization to a vanilla density optimization for Al clusters
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Flowcharts for (a) a density optimization job and (b) a TD‐OFDFT job (see text for details). On the side of the flowcharts, we indicate in green boxes the names of the Python classes involved
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Mg8 KSDFT virtual orbitals: LUMO, LUMO+2, and LUMO+6 (above). OFDFT “collective” virtual orbitals: LUMO, LUMO+3, and LUMO+6 (below)
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Comparison of optical spectra computed with TD‐OFDFT and TD‐KSDFT for Mg50. A view of the total density is given in the inset
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Comparison of optical spectra obtained with TD‐OFDFT and TD‐KSDFT for Mg8. A view of the total density is given in the inset
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Pair distribution functions g(r) for liquid Al at experimental conditions compared to X‐ray diffraction data61
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Computer and Information Science > Computer Algorithms and Programming
Electronic Structure Theory > Density Functional Theory
Software > Quantum Chemistry

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