Theoretical study of thermal stability range of α″–Fe16N2 within the iron nitride binary phase diagram

α″–Fe16N2 has been investigated as one of promising candidates for environment-friendly magnets. While giant saturation magnetization has previously been experimentally observed and recently explained by the Cluster+Atom model in α″–Fe16N2, its magnetic anisotropy and structural stability leave room for improvement. Recent theoretical studies have considered alloying Fe16N2 with various elements to improve the magnetic properties and/or stability against decomposition. However, estimates of stability in particular are typically restricted to simple ground-state-energy comparisons, effectively taken at 0 K. For a more practical measure of stability, we therefore extend ground-state energies, obtained with the plane-wave density-functional theory (DFT) code Quantum ESPRESSO, with appropriate empirical and/or statistical corrections to obtain free energies at arbitrary temperature. We then compare the stability of Fe16N2 against the neighboring phases and phase combinations in the Fe–N binary system within the Compound Energy Formalism, to estimate the range of temperatures at which it is stable. With simple empirical correction terms for energy of N2 gas, Fe16N2 may be predicted to persist well above 800 K. With statistical correction terms that estimate contributions from solid phases, we instead find Fe16N2 to be stable up to ∼425 K, yielding to α–Fe + ε–Fe3N at higher temperatures. We compare against experimental observations of the Fe–N phase diagram including Fe16N2 decomposition at around 500 K, and discuss the effect and relative accuracy of different correction terms.

Authors

Institutions

Publication Details

Journal
AIP Advances
Published
2025-03-01
DOI
https://doi.org/10.1063/9.0000916
Citations
4
Primary Topic
Magnetic Properties of Alloys
Type
article
Field-Weighted Citation Impact
1.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Theoretical study of thermal stability range of α″–Fe16N2 within the iron nitride binary phase diagram

Peter Stoeckl, Jian‐Ping Wang
4 citations
AIP Advances
Magnetic Properties of Alloys
1.71
article

Theoretical study of thermal stability range of α″–Fe16N2 within the iron nitride binary phase diagram

Peter Stoeckl, Jian‐Ping Wang
article en
4 citations

Abstract

α″–Fe16N2 has been investigated as one of promising candidates for environment-friendly magnets. While giant saturation magnetization has previously been experimentally observed and recently explained by the Cluster+Atom model in α″–Fe16N2, its magnetic anisotropy and structural stability leave room for improvement. Recent theoretical studies have considered alloying Fe16N2 with various elements to improve the magnetic properties and/or stability against decomposition. However, estimates of stability in particular are typically restricted to simple ground-state-energy comparisons, effectively taken at 0 K. For a more practical measure of stability, we therefore extend ground-state energies, obtained with the plane-wave density-functional theory (DFT) code Quantum ESPRESSO, with appropriate empirical and/or statistical corrections to obtain free energies at arbitrary temperature. We then compare the stability of Fe16N2 against the neighboring phases and phase combinations in the Fe–N binary system within the Compound Energy Formalism, to estimate the range of temperatures at which it is stable. With simple empirical correction terms for energy of N2 gas, Fe16N2 may be predicted to persist well above 800 K. With statistical correction terms that estimate contributions from solid phases, we instead find Fe16N2 to be stable up to ∼425 K, yielding to α–Fe + ε–Fe3N at higher temperatures. We compare against experimental observations of the Fe–N phase diagram including Fe16N2 decomposition at around 500 K, and discuss the effect and relative accuracy of different correction terms.

AIP AdvancesVol. 15(3)
University of Minnesota (US)
Openalex Percentile: Top 17%
Magnetic Properties of Alloys
1.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.