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

Amit Samanta, Prasanna Balaprakash, Sylvie Aubry, Brian K. Lin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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. 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.

Original languageEnglish
Article number115862
JournalScripta Materialia
Volume241
DOIs
StatePublished - Mar 1 2024
Externally publishedYes

Funding

This research was supported by the High Performance Computing for Manufacturing Program ( HPC4Mfg ), managed by the U.S. Department of Energy 's Advanced Materials and Manufacturing Technologies Office within the Energy Efficiency and Renewable Energy Office. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Release number: LLNL-JRNL-844794.

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