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Applicability Analysis of Molecular Statics simulation for Nano-materials

2013· article· en· W2081271466 on OpenAlexaff
Enming Miao, Zhishang Xu, Yang Ni, Jichao Miao, Guannua Li

Bibliographic record

VenueTELKOMNIKA Indonesian Journal of Electrical Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStaticsMolecular dynamicsStatistical physicsModulusMaterials scienceMolecular modelNanotechnologyPhysicsComputer scienceChemistryClassical mechanicsComputational chemistryComposite material

Abstract

fetched live from OpenAlex

Molecular statics can simulate the process without time limitation, while molecular dynamics can only study the motion of system in small time range. Therefore, it’s necessary to give attention and research on the application of molecular statics in engineering materials field. In order to investigate the applicability analysis of molecular statics simulation in nanomaterials scientific research, taking 3D copper monocrystal as the research object,the corresponding molecular statics model was established, the stress-strain data and Young's modulus at the absolute-zero temperature is got.Comparing the result of molecular static simulation with molecular dynamics simulation and the known experimental data, the results show that the model established by molecular statics can explain the mechanical properties of copper monocrystal subjected to tensile loading. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4666 Full Text: PDF

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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venueTELKOMNIKA Indonesian Journal of Electrical EngineeringSame topicBoron and Carbon Nanomaterials ResearchFrench-language works237,207