Static Subspace Approximation for Random Phase Approximation Correlation Energies: Applications to Materials for Catalysis and Electrochemistry DOI
Jacob M. Clary, Olivia Hull, Daniel Weinberg

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Modeling complex materials using high-fidelity, ab initio methods at low cost is a fundamental goal for quantum chemical software packages. The GW approximation and random phase (RPA) provide unified description of both electronic structure total energies the same physics in many-body perturbative approach that can be more accurate than generalized-gradient density functional theory (DFT) methods. However, GW/RPA implementations have historically been limited to either specific classes or application toward small systems. static subspace allows reduced full-frequency calculations has previously benchmarked thoroughly calculations. Here, we describe our including partial occupations orbitals RPA study electrocatalysts. We energy across diverse test suite variety computational parameters. benchmarking quantifies impact different extrapolation procedures representing polarizability infinite screened cutoff, shows cutoffs above 20-25 Ryd result diminishing accuracy returns predicting energies. Additionally, moderately sized electrocatalytic models, 2-3 times fewer resources are used compute by with 20-30% basis, an error approximately 0.01 eV better adsorption Finally, show these electrochemical models shift DFT shifts up 0.5 frequently eigenvalues surface adsorbate states 0.5-1 eV.

Language: Английский

Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach DOI Creative Commons

Oz Y. Mendelsohn,

Michal Hartstein,

Stefan Chmiela

et al.

Chemical Physics Letters, Journal Year: 2025, Volume and Issue: unknown, P. 141964 - 141964

Published: Feb. 1, 2025

Language: Английский

Citations

0
Static Subspace Approximation for Random Phase Approximation Correlation Energies: Applications to Materials for Catalysis and Electrochemistry DOI
Jacob M. Clary, Olivia Hull, Daniel Weinberg

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Modeling complex materials using high-fidelity, ab initio methods at low cost is a fundamental goal for quantum chemical software packages. The GW approximation and random phase (RPA) provide unified description of both electronic structure total energies the same physics in many-body perturbative approach that can be more accurate than generalized-gradient density functional theory (DFT) methods. However, GW/RPA implementations have historically been limited to either specific classes or application toward small systems. static subspace allows reduced full-frequency calculations has previously benchmarked thoroughly calculations. Here, we describe our including partial occupations orbitals RPA study electrocatalysts. We energy across diverse test suite variety computational parameters. benchmarking quantifies impact different extrapolation procedures representing polarizability infinite screened cutoff, shows cutoffs above 20-25 Ryd result diminishing accuracy returns predicting energies. Additionally, moderately sized electrocatalytic models, 2-3 times fewer resources are used compute by with 20-30% basis, an error approximately 0.01 eV better adsorption Finally, show these electrochemical models shift DFT shifts up 0.5 frequently eigenvalues surface adsorbate states 0.5-1 eV.

Language: Английский

Citations

0