SearcharxivSearch

arXiv subjects

Caden Ginter

Publications and source records attributed to Caden Ginter.

2 recordsLinked to original sources

Systematic dynamical mean-field theory study of 3d perovskite oxides with uniform Coulomb interactions

Strongly correlated transition-metal perovskite oxides pose a fundamental challenge for electronic-structure theory and for large-scale, data-driven materials discovery. While DFT+DMFT provides a quantitatively accurate description of such systems, its high-throughput application is hindered by the need to determine material-specific Coulomb interaction parameters ($U$). First-principles approaches such as the cRPA predict a highly nonlinear and non-transferable evolution of the interaction strength across chemically similar ABO$_3$ perovskites. Here we show that this paradigm does not extend to the large-energy-window eDMFT, which employs highly localized orbitals and treats electronic correlations and screening self-consistently within the same many-body framework. As a result, spectral properties are governed primarily by the dynamical self-energy rather than by static interaction-induced energy shifts. Recent constrained-eDMFT calculations demonstrated that, for broad classes of $3d$ transition-metal oxides, the self-consistently screened Coulomb interactions naturally fall within relatively narrow ranges for correlated metals and insulators. Motivated by these findings, we implement a high-throughput eDMFT framework employing physically derived interaction values of $U=6$ eV for metals and $U=10$ eV for insulators together with $exact$ double counting. We test this framework using systematic high-throughput eDMFT calculations for ABO$_3$ compounds (A = Ca, Sr, La; B = V--Ni) and benchmark the resulting spectral functions against photoemission experiments, where we find overall excellent agreement. Our results establish that charge self-consistent eDMFT enables robust, parameter-tuning-free high-throughput many-body calculations for correlated oxides, opening a practical pathway toward predictive electronic-structure databases for strongly correlated materials.

cond-mat.str-el

Accelerated prediction of dielectric functions in solar cell materials with graph neural networks

We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line shapes, while enabling efficient high-throughput screening. Applied to the recently developed Alexandria materials database, containing over four hundred thousand insulating materials, we uncover a clear elemental trend, with vanadium emerging as a strong indicator of materials with high-spectroscopic limited maximum efficiency (SLME). In particular, vanadium-based perovskite materials show a substantially higher fraction of high-SLME compounds compared to the database average, underscoring their promise for optoelectronic applications.

cond-mat.mtrl-sci