Atomistic modeling of thermo‐mechanical properties of cubic SiC
Bibliographic record
Abstract
Abstract SiC is an important multifunctional material with application in electronics and as a structural material. Many investigations of SiC have been done using both classical molecular dynamics and first principles methods. However, they are of limited scope and, in particular, SiC properties at finite temperatures have not been adequately evaluated. The good mechanical, thermal, and chemical properties of SiC such as high stiffness, high hardness, high mechanical strength at high temperature, and high thermal conductivity, make SiC a candidate for various applications in nuclear industries. In this work, we evaluated thermomechanical properties at finite temperatures obtained by LAMMPS code with traditionally used Tersoff potential (TR89 with PRB 41 correction), and the newer GW 2002 (GW02) potential. We compared them with the calculations made using MEAM 1995 (MEAM 95) and with our first principles DFT predictions. It is demonstrated that the thermal expansion and mechanical properties calculated as a function of temperature for classical potentials TR89 and GW02 do not agree well with first principles calculations while better agreement is found for the MEAM95 potential. Classical molecular dynamics calculations made with the use of two earlier potentials under‐predict thermal conductivity by one order of magnitude for the TR89 potential and by more than 30% for the GW02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".