Atomistic study of atomic diffusion in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi mathvariant="normal">L</mml:mi> <mml:msub> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:msub> <mml:mtext>−</mml:mtext> <mml:msub> <mml:mi>FeNi</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> : Magnetochemical excitations and compositional effects
Bibliographic record
Abstract
Atomic diffusion in intermetallic phases is crucial for the microstructural evolution and mechanical properties of transition-metal alloys. This study investigates diffusion in $\mathrm{L}{1}_{2}\text{\ensuremath{-}}{\mathrm{FeNi}}_{3}$ using kinetic Monte Carlo simulations with an effective interaction model parametrized on density functional theory data. By explicitly modeling atomic and spin degrees of freedom, the simulations systematically explore the effects of finite-temperature magnetochemical interplay and stoichiometry on diffusion. The results show that Fe diffuses slightly faster than Ni, contrasting the empirical rule experimentally verified in many intermetallics where the majority element diffuses faster. Non-Arrhenius behaviors of Fe and Ni diffusion are found, which are mainly attributed to the chemical rather than magnetic transitions. Activation energies for Fe and Ni atoms are predicted to be 3.22 eV in the ferromagnetic $\mathrm{L}{1}_{2}$ phase, approximately 0.6 eV higher than in the paramagnetic disordered phase. Magnetic excitations significantly impact diffusion properties, with notable discrepancies between the equilibrium ferromagnetic and perfectly ferromagnetic states in the $\mathrm{L}{1}_{2}$ phase. Deviations from stoichiometric composition lower activation energies and enhance diffusion for both Fe and Ni, which is unexpectedly similar to the trends observed in B2-type intermetallics. In the absence of experimental tracer diffusion data for the $\mathrm{L}{1}_{2}$ phase, this work provides reliable diffusion predictions based on a model validated in disordered Fe-Ni alloys. These results are expected to guide future studies of phase transformation and domain growth in ordered intermetallics. This work provides a detailed analysis of diffusion in $\mathrm{L}{1}_{2}\text{\ensuremath{-}}{\mathrm{FeNi}}_{3}$, contributing a broader understanding of magnetochemical and compositional effects on diffusion behavior in intermetallic alloys.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".