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Record W4391823961 · doi:10.1021/acs.jctc.3c01230

Low-Scaling <i>GW</i> Algorithm Applied to Twisted Transition-Metal Dichalcogenide Heterobilayers

2024· article· en· W4391823961 on OpenAlexfundno aff
Maximilian Graml, Klaus Zollner, Daniel Hernangómez‐Pérez, Paulo E. Faria, Jan Wilhelm

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2024
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsnot available
FundersEuropean Research CouncilGauss Centre for SupercomputingAzrieli FoundationDeutsche ForschungsgemeinschaftLeibniz-GemeinschaftUniversität RegensburgMinerva FoundationLeibniz-RechenzentrumEuropean CommissionGraphene Flagship
KeywordsScalingTransition metalAlgorithmComputer sciencePhysicsComputational scienceMaterials scienceNanotechnologyChemistryMathematicsGeometry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The GW method is widely used for calculating the electronic band structure of materials. The high computational cost of GW algorithms prohibits their application to many systems of interest. We present a periodic, low-scaling, and highly efficient GW algorithm that benefits from the locality of the Gaussian basis and the polarizability. The algorithm enables G 0 W 0 calculations on a MoSe 2 /WS 2 bilayer with 984 atoms per unit cell, in 42 h using 1536 cores. This is 4 orders of magnitude faster than a plane-wave G 0 W 0 algorithm, allowing for unprecedented computational studies of electronic excitations at the nanoscale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.256
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2024
Admission routes1
Has abstractyes

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