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Entropic Density Functional Theory
by
Yousefi, Ahmad
, Caticha, Ariel
in
Approximation
/ Density functional theory
/ Density functionals
/ entropic inference
/ Entropy
/ Expected values
/ Hohenberg–Kohn theorem
/ Inequality
/ inhomogeneous fluids
/ Maximum entropy method
/ method of maximum entropy
/ Particle density (concentration)
/ Probability
/ Probability distribution
2024
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Entropic Density Functional Theory
by
Yousefi, Ahmad
, Caticha, Ariel
in
Approximation
/ Density functional theory
/ Density functionals
/ entropic inference
/ Entropy
/ Expected values
/ Hohenberg–Kohn theorem
/ Inequality
/ inhomogeneous fluids
/ Maximum entropy method
/ method of maximum entropy
/ Particle density (concentration)
/ Probability
/ Probability distribution
2024
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Do you wish to request the book?
Entropic Density Functional Theory
by
Yousefi, Ahmad
, Caticha, Ariel
in
Approximation
/ Density functional theory
/ Density functionals
/ entropic inference
/ Entropy
/ Expected values
/ Hohenberg–Kohn theorem
/ Inequality
/ inhomogeneous fluids
/ Maximum entropy method
/ method of maximum entropy
/ Particle density (concentration)
/ Probability
/ Probability distribution
2024
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Journal Article
Entropic Density Functional Theory
2024
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Overview
A formulation of density functional theory (DFT) is constructed as an application of the method of maximum entropy for an inhomogeneous fluid in thermal equilibrium. The use of entropy as a systematic method to generate optimal approximations is extended from the classical to the quantum domain. This process introduces a family of trial density operators that are parameterized by the particle density. The optimal density operator is that which maximizes the quantum entropy relative to the exact canonical density operator. This approach reproduces the variational principle of DFT and allows a simple proof of the Hohenberg–Kohn theorem at finite temperature. Finally, as an illustration, we discuss the Kohn–Sham approximation scheme at finite temperature.
Publisher
MDPI AG
Subject
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