Understanding the influence of hydrogen on BCC iron grain boundaries using the kinetic activation relaxation technique (k-ART)
Bibliographic record
Abstract
Abstract Hydrogen embrittlement (HE) poses a significant challenge to the mechanical integrity of iron and its alloys. This study explores the influence of hydrogen atoms on two distinct grain boundaries (GBs), Σ 37 and Σ 3 , in body-centered-cubic (BCC) iron. Using the kinetic activation relaxation technique , an off-lattice kinetic Monte Carlo approach with an EAM-based potential, extensive catalogs of activated events for atoms in both H-free and H-saturated GBs were generated. Studying the diffusion of H, we find that, for these systems, while GB is energetically favorable for H, this element diffuses more slowly at the GBs than in the bulk. The results further indicate that the Σ 3 GB exhibits higher stability in its pure form compared to the Σ 37 GB, with notable differences in energy barriers and diffusion behaviors. Moreover, with detailed information about the evolution landscape around the GB, we find that the saturation of a GB with hydrogen both stabilizes the GB by shifting barriers associated with Fe diffusion to higher energies and reducing the number of diffusion events. For the Σ 37 GB, the presence of hydrogen causes elastic deformation, affecting the diffusion of Fe atoms both at the GB and in adjacent positions. This results in new diffusion pathways but with higher diffusion barriers, unlike for the Σ 3 GB. These results indicate that the presence of hydrogen rigidifies the direct GB interface layers while allowing more atoms to be active for the Σ 37 GB. This provides a microscopic basis to support the existence of competing mechanisms compatible with either plasticity (such as hydrogen enhanced localized plasticity—HELP) or energy-dominated (hydrogen enhanced decohesion mechanism—HEDE) embrittlement, with the relative importance of these mechanisms determined by the local geometry of the GBs.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".