首页|Findings from Lawrence Livermore National Laboratory in the Area of Machine Lear ning Reported (Machine-learning-aided Density Functional Theory Calculations of Stacking Fault Energies In Steel)
Findings from Lawrence Livermore National Laboratory in the Area of Machine Lear ning Reported (Machine-learning-aided Density Functional Theory Calculations of Stacking Fault Energies In Steel)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting from Livermore, California, by NewsRx journalists, research stated, "A combined large-scale first principle s approach with machine learning and materials informatics is proposed to quickl y sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and mangan ese." Funders for this research include High Performance Computing for Manufacturing P rogram (HPC4Mfg), United States Department of Energy (DOE), United States Depart ment of Energy (DOE). The news correspondents obtained a quote from the research from Lawrence Livermo re National Laboratory, "A systematic exploration of the distribution of aluminu m and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computation al cost of exploring the composition space, this process is sped up using an aut omated machine learning tool: DeepHyper. Our results predict that it is energeti cally favorable for Al to stay away from a stacking fault, but Mn atoms do not a ffect the stacking fault energy and can stay in the vicinity of the fault." According to the news reporters, the research concluded: "The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their int eractions on the equilibrium distribution are systematically analyzed."
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