High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales.We propose a workflow that couples highly relevant physics into machine learning(ML)to predict proper...High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales.We propose a workflow that couples highly relevant physics into machine learning(ML)to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength.We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset.Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism.展开更多
基金This research used resources of the Compute and Data Environment for Science(CADES)at the Oak Ridge National Laboratory,which is supported by the Office of Science of the US Department of Energy under Contract No.DE-AC05-00OR22725This paper has been authored by UT-Battelle,LLC,under contract DE-AC05-00OR22725 with the US Department of Energy(DOE).
文摘High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales.We propose a workflow that couples highly relevant physics into machine learning(ML)to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength.We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset.Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism.
