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Record W3169850114 · doi:10.32920/ryerson.14656692.v1

Variable time domain discretization methodology for molecular dynamics simulation of metallic compounds

2021· preprint· en· W3169850114 on OpenAlexaff
Jamal Zeinalov

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMolecular dynamicsDiscretizationWork (physics)Computer scienceVariable (mathematics)Time domainComputationKinetic energyStatistical physicsSimulationComputational chemistryChemistryAlgorithmMechanical engineeringPhysicsMathematicsClassical mechanicsEngineering

Abstract

fetched live from OpenAlex

The present work proposes a methodology to improve the computational requirements of molecular dynamics simulations while maintaining or improving the fidelity of the obtained results. The most common method of molecular dynamics simulation at present is the multi-force, constant time-step, explicit computation, which advances a single time step at a time to determine the next state of the system. The present work proposes a variable time-step strategy, where a single large simulation is subdivided into multiple time domains which redistribute computational resources where they are needed the most: in areas of higher than average potential or kinetic energy or highly dynamic areas around impurity clusters, void formations and crack propagations. The research focuses on the simulation of metallic compounds, as these form the basis of most common molecular dynamics simulations, and have been very thoroughly investigated over the years, thus providing a very extensive body of work for the purpose of comparison and validation of the proposed methodology. The novel methodology presented in this work allows to alleviate some of the limitations associated with the molecular dynamics methodologies and go beyond traditional scales of simulation. The proposed method has been observed to deliver 5 to 20 percent increase in simulation size domain while maintaining or improving the accuracy and computational cycle time. The benefits were observed to be greater for large simulations with one or more areas of higher than average kinetic or potential energy levels, such as those found during crack initiation and propagation, coating-substrate interface, localized pressure application or large thermal gradient. The large difference allows for very clear prioritization of computational resources for high energy areas and as a result provides for faster and more accurate simulation even with increased domain size. Conversely, this method has been observed to provide little to no benefit when simulating stable systems that are undergoing very slow change, such as (relatively) slow change in ambient temperature or pressure, or otherwise homogeneous internal and external boundary conditions. However, for the majority of applications described above, including coating deposition and additive manufacturing, the proposed methodology will yield substantial increase in both simulation size and accuracy, since in the aforementioned processes kinetic and potential energy gradients across the simulation are typically very significant

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.312
Teacher spread0.285 · 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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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