Machine-learning-aided density functional theory calculations of stacking fault energies in steel

Journal Article · Sat Nov 18 00:00:00 EST 2023 · Scripta Materialia

DOI:https://doi.org/10.1016/j.scriptamat.2023.115862· OSTI ID:2217425

Samanta, Amit ^[1]; Balaprakash, Prasanna ^[2]; Aubry, Sylvie  ^[1]; Lin, Brian K. ^[3]

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1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
2. Argonne National Laboratory (ANL), Argonne, IL (United States)
3. ArcelorMittal Global R&D, Chicago, IN (United States)

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Here our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO)
• Grant/Contract Number: AC52-07NA27344; LLNL-JRNL-844794
• OSTI ID: 2217425
• Alternate ID(s): OSTI ID: 2217250; OSTI ID: 2222563
• Report Number(s): LLNL-JRNL-846038; LLNL-JRNL-844794; 1070041
• Journal Information: Scripta Materialia, Vol. 241, Issue N/A; ISSN 1359-6462
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE