REVIEW 4 cited by
Generalized Gradient Approximation Made Thermal
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Using the methodology of conditional-probability density functional theory, and several mild assumptions, we calculate the temperature-dependence of the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA). This numerically-defined thermal GGA reduces to the local approximation in the uniform limit and PBE at zero temperature, and can be fit reasonably accurately (within 8%) assuming the temperature-dependent enhancement is independent of the gradient. This locally thermal PBE satisfies both the coordinate-scaled correlation inequality and the concavity condition, which we prove for finite temperatures. The temperature dependence differs markedly from existing thermal GGA's.
Forward citations
Cited by 4 Pith papers
-
Two-legged approximation for building non-empirical hybrids and analyzing correlation at finite temperature
A finite-temperature two-legged adiabatic connection construction provides a temperature- and density-dependent hybrid mixing parameter, demonstrated on the uniform electron gas and asymmetric Hubbard dimer.
-
Chemical potential of the warm dense electron gas from ab initio path integral Monte Carlo simulations
Direct PIMC simulations yield the exchange-correlation chemical potential of the warm dense uniform electron gas, cross-validating the GDSMFB free-energy parametrization.
-
{\eta}-ensemble path integral Monte Carlo approach to the free energy of the warm dense electron gas and the uniform electron liquid
The eta-ensemble PIMC method gives direct free energies for the uniform electron gas, yielding new data for strongly coupled low-density conditions and matching the Groth et al. parametrization at r_s less than or equ...
-
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
MALA predicts electron densities and energies from local atomic environments using trained neural networks, reaching system sizes beyond standard DFT.
Discussion (0). Continue with ORCID to comment.