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- W3138193891 abstract "The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials. In practice, however, DFT hinges on approximate (free-)energy functionals from which density profiles (and hence the thermodynamic potential) follow via an Euler-Lagrange equation. Here we explore a relatively simple Machine Learning (ML) approach to improve the standard mean-field approximation of the excess Helmholtz free energy functional of a 3D Lennard-Jones system at a supercritical temperature. The learning set consists of density profiles from grand-canonical Monte Carlo simulations of this system at varying chemical potentials and external potentials in a planar geometry only. Using the DFT formalism we nevertheless can extract not only very accurate 3D bulk equations of state but also radial distribution functions using the Percus test-particle method. Unfortunately, our ML approach did not provide very reliable Ornstein-Zernike direct correlation functions for small distances." @default.
- W3138193891 created "2021-03-29" @default.
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- W3138193891 date "2021-03-01" @default.
- W3138193891 modified "2023-10-01" @default.
- W3138193891 title "Machine-learning free-energy functionals using density profiles from simulations" @default.
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- W3138193891 doi "https://doi.org/10.1063/5.0042558" @default.
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