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Record W2933430141 · doi:10.1088/1361-651x/ab309f

A new approach for electronic heat conduction in molecular dynamics simulations

2019· article· en· W2933430141 on OpenAlexafffund

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute Canada
KeywordsThermal conductionMolecular dynamicsHeat transferMassively parallelNanoscopic scalePhononEnergy (signal processing)ElectronElectronics

Abstract

fetched live from OpenAlex

Abstract We present a new approach for the two-temperature molecular dynamics (MD) model for coupled simulations of electronic and phonon heat conduction in nanoscale systems. The proposed method uses a master equation to perform heat conduction of the electronic temperature eschewing the need to use a basis set to evaluate operators. This characteristic allows us to seamlessly couple the electronic heat conduction model with MD codes without the need to introduce an auxiliary mesh. We implemented the methodology in the large-scale atomic/molecular massively parallel simulator code, and through multiple examples, we validated the methodology. We then study the effect of electron–phonon interaction in high energy irradiation simulations and the effect of laser pulse on metallic materials. We show that the model provides an atomic level description in complex geometries of energy transfer between electrons and phonons. Thus, the proposed approach provides an alternative way to the two-temperature MD models. The parallel performance and some aspects of the implementation are presented.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.232
Teacher spread0.220 · 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
GenreEmpirical

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

Citations12
Published2019
Admission routes2
Has abstractyes

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