ArticleScience and technology of advanced materials2020
Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning.
Article in Science and technology of advanced materials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods.Materials (Basel, Switzerland) · 2026Article
- Corrosion fatigue life prediction of multiple austenitic stainless steel grades in high-temperature, high-pressure water environments using a GA-optimized neural network.Scientific reports · 2025Article
- Application of comprehensive geriatric assessment in oncology nursing: A literature review on optimizing treatment decisions and patient outcomes.World journal of clinical oncology · 2025Review
- Investigations of Metallurgical Differences in AISI 347 and Their Influence on Deformation and Transformation Behaviour and Resulting Fatigue Life.Materials (Basel, Switzerland) · 2024Article
- Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning.Science and technology of advanced materials · 2024Article
- Design metastability in high-entropy alloys by tailoring unstable fault energies.Science advances · 2022Article
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2 authors.
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Abstract
Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition.
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