The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using...The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using small data sets.Based on an unprecedented large dataset containing 1252 multicomponent alloys,machine-learning methods showed that the formation of solid solutions can be very accurately predicted(93%).The machine-learning results help identify the most important features,such as molar volume,bulk modulus,and melting temperature.展开更多
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Research performed by Leidos Research Support Team staff was conducted under the RSS contract 89243318CFE000003This research was supported in part by an appointment to the U.S.Department of Energy(DOE)Postgraduate Research Program at the National Energy Technology Laboratory(NETL)administered by the Oak Ridge Institute for Science and EducationThis research used resources of Oak Ridge National Laboratory’s Compute and Data Environment for Science(CADES)and the Oak Ridge Leadership Computing Facility,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725.
文摘The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using small data sets.Based on an unprecedented large dataset containing 1252 multicomponent alloys,machine-learning methods showed that the formation of solid solutions can be very accurately predicted(93%).The machine-learning results help identify the most important features,such as molar volume,bulk modulus,and melting temperature.
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.
