Collinear-spin machine learned interatomic potential for Fe7Cr2Ni alloy

2024/03/22 by Lakshmi Shenoy, Christopher D. Woodgate, J. B. Staunton +4 · 1 voice · 3 citations
Materials Science · #Machine Learning in Materials Science #X-ray Diffraction in Crystallography #Hydrogen embrittlement and corrosion behaviors in metals

paper · pdf · doi:10.1103/physrevmaterials.8.033804

openalex publication_date 2024/03/22 · openalex created_date 2024/03/23 · openalex updated_date 2026/08/01

Abstract

We have developed a machine learned interatomic potential for the prototypical austenitic steel <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msub><a:mi>Fe</a:mi><a:mn>7</a:mn></a:msub><a:msub><a:mi>Cr</a:mi><a:mn>2</a:mn></a:msub><a:mi>Ni</a:mi></a:mrow></a:math>, using the Gaussian approximation potential (GAP) framework. This GAP can model the alloy's properties with close to density functional theory (DFT) accuracy, while at the same time allowing us to access larger length and time scales than expensive first-principles methods. We also extended the GAP input descriptors to approximate the effects of collinear spins (spin GAP), and demonstrate how this extended model successfully predicts structural distortions due to antiferromagnetic and paramagnetic spin states. We demonstrate the application of the spin GAP model for bulk properties and vacancies and validate against DFT. These results are a step towards modeling the atomistic origins of ageing in austenitic steels with higher accuracy. Published by the American Physical Society 2024

Citations

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Cited by

• Competition between phase ordering and phase segregation in the TixNbMoTaW and TixVNbMoTaW refractory high-entropy alloys
• Structure, short-range order, and phase stability of the AlxCrFeCoNi high-entropy alloy: insights from a perturbative, DFT-based analysis
• BraWl: Simulating the thermodynamics and phase stability of multicomponent alloys using conventional and enhanced sampling techniques

Discussions

• More recently, I have been using #MachineLearning tools to develop #InteratomicPotentials: doi.org/10.1103/Phys... This work developed an MLIP for the prototypical austenitic #StainlessSteel, Fe-Cr- [bsky, 0 points, 0 comments]

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