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- W3092439557 abstract "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 computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms." @default.
- W3092439557 created "2020-10-15" @default.
- W3092439557 creator A5023482297 @default.
- W3092439557 creator A5034807923 @default.
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- W3092439557 creator A5049799806 @default.
- W3092439557 creator A5058200721 @default.
- W3092439557 creator A5086674871 @default.
- W3092439557 date "2021-07-08" @default.
- W3092439557 modified "2023-10-06" @default.
- W3092439557 title "Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks" @default.
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- W3092439557 doi "https://doi.org/10.1103/physrevb.104.035120" @default.