Carbon adsorption on and diffusion through the Fe(110) surface and in bulk: Developing a new strategy for the use of empirical potentials in complex material set‐ups
Bibliographic record
Abstract
Oil and gas infrastructures are submitted to extreme conditions and off‐shore rigs and petrochemical installations require expensive high‐quality materials to limit damaging failures. Yet, due to a lack of microscopic understanding, most of these materials are developed and selected based on empirical evidence leading to over‐qualified infrastructures. Computational efforts are necessary, therefore, to identify the link between atomistic and macroscopic scales and support the development of better targeted materials for this and other energy industry. As a first step towards understanding carburization and metal dusting, we assess the capabilities of an embedded atom method (EAM) empirical force field as well as those of a ReaxFF force field using two different parameter sets to describe carbon diffusion at the surface of Fe, comparing the adsorption and diffusion of carbon into the 110 surface and in bulk of α‐iron with equivalent results produced by density functional theory (DFT). The EAM potential has been previously used successfully for bulk Fe–C systems. Our study indicates that preference for C adsorption site, the surface to subsurface diffusion of C atoms and their migration paths over the 110 surface are in good agreement with DFT. The ReaxFF potential is more suited for simulating the hydrocarbon reaction at the surface while the subsequent diffusion to subsurface and bulk is better captured with the EAM potential. This result opens the door to a new approach for using empirical potentials in the study of complex material set‐ups.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".