Reconciling ionization energies and band gaps of warm dense matter derived with ab initio simulations and average atom models

Abstract

Average atom (AA) models allow one to efficiently compute electronic and optical properties of materials over a wide range of conditions and are often employed to interpret experimental data. However, at high pressure, predictions from AA models have been shown to disagree with results from ab initio computer simulations. Here we reconcile these deviations by developing an innovative type of AA model, AVION, that computes the electronic eigenstates with novel boundary conditions within the ion sphere. Bound and free states are derived consistently. We drop the common AA image that the free-particle spectrum starts at the potential threshold, which we found to be incompatible with ab initio calculations. We perform ab initio simulations of crystalline and liquid carbon and aluminum over a wide range of densities and show that the computed band structure is in very good agreement with predictions from AVION.

References in corpus (5)

• Understanding Jupiter's Interior
• Equation of state for partially ionized carbon at high temperatures
• Multiple Scattering Theory for Dense Plasmas
• Calculation of electron-ion temperature equilibration rates and friction coefficients in plasmas and liquid metals using quantum molecular dynamics
• The structure in warm dense carbon

Cited by in corpus (9)

• Electronic Density Response of Warm Dense Matter
• First principles simulations of dense hydrogen
• Non-empirical mixing coefficient for hybrid XC functionals from analysis of the XC kernel
• First-principles derivation and properties of density-functional average-atom models
• Improved calculations of mean ionization states with an average-atom model
• Dense plasma opacity via the multiple-scattering method
• Ionization potential depression and Fermi barrier in warm dense matter--a first--principles approach
• Training-free hyperparameter optimization of neural networks for electronic structures in matter
• Physics-enhanced neural networks for equation-of-state calculations