Resolving finite-size errors in EOM-CCSD band gaps of solids with interacting-bath dynamical embedding theory

Periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD) has shown promise for quantitative calculations of band structures in solids. However, its steep computational scaling has limited calculations to relatively coarse k-point meshes, leading to sizable finite-size errors and discrepant estimates of thermodynamic-limit band gaps in recent benchmarks. In this work, we revisit EOM-CCSD band gaps for ten semiconductors and insulators using interacting-bath dynamical embedding theory (ibDET), a systematically improvable Green's function embedding framework that enables dense Brillouin-zone sampling at modest computational cost. By pushing the k-point sampling up to 10 × 10 × 10, well beyond the system sizes accessible in canonical periodic EOM-CCSD calculations, we significantly reduce finite-size errors and obtain stable thermodynamic-limit extrapolations. We further compare G0W0@PBE, G0W0@HF, and EOM-CCSD on an equal footing using the same numerical settings in PySCF. We find that EOM-CCSD yields a mean absolute error of 0.32 eV relative to experimental band gaps for a test set of ten semiconductors and insulators, which is lower than that of G0W0@PBE. For ZnO, EOM-CCSD also accurately describes the Zn 3d-band binding energy, despite overestimating the band gap. These results demonstrate that ibDET offers a practical route to high-accuracy many-body electronic structure calculations in periodic systems.

Authors

Institutions

Publication Details

Journal
The Journal of Chemical Physics
Published
2026-09-01
DOI
https://doi.org/10.1063/5.0347878
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Resolving finite-size errors in EOM-CCSD band gaps of solids with interacting-bath dynamical embedding theory

Christopher Hillenbrand, Christian Venturella, Enzhi Chen, Tianyu Zhu et al.
The Journal of Chemical Physics
Machine Learning in Materials Science
article

Resolving finite-size errors in EOM-CCSD band gaps of solids with interacting-bath dynamical embedding theory

Christopher Hillenbrand, Christian Venturella, Enzhi Chen, Tianyu Zhu, Jiachen Li
article en

Abstract

Periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD) has shown promise for quantitative calculations of band structures in solids. However, its steep computational scaling has limited calculations to relatively coarse k-point meshes, leading to sizable finite-size errors and discrepant estimates of thermodynamic-limit band gaps in recent benchmarks. In this work, we revisit EOM-CCSD band gaps for ten semiconductors and insulators using interacting-bath dynamical embedding theory (ibDET), a systematically improvable Green's function embedding framework that enables dense Brillouin-zone sampling at modest computational cost. By pushing the k-point sampling up to 10 × 10 × 10, well beyond the system sizes accessible in canonical periodic EOM-CCSD calculations, we significantly reduce finite-size errors and obtain stable thermodynamic-limit extrapolations. We further compare G0W0@PBE, G0W0@HF, and EOM-CCSD on an equal footing using the same numerical settings in PySCF. We find that EOM-CCSD yields a mean absolute error of 0.32 eV relative to experimental band gaps for a test set of ten semiconductors and insulators, which is lower than that of G0W0@PBE. For ZnO, EOM-CCSD also accurately describes the Zn 3d-band binding energy, despite overestimating the band gap. These results demonstrate that ibDET offers a practical route to high-accuracy many-body electronic structure calculations in periodic systems.

The Journal of Chemical PhysicsVol. 165(9)
Yale University (US)
National Science Foundation, National Defense Science and Engineering Graduate
Openalex Percentile: Top 57%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.