首页|New Machine Learning Research from Chengdu University Discussed (Machine learning guided BCC or FCC phase prediction in high entropy alloys)
New Machine Learning Research from Chengdu University Discussed (Machine learning guided BCC or FCC phase prediction in high entropy alloys)
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NETL
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Elsevier
New research on artificial intelligence is the subject of a new report. According to news reporting from Chengdu, People's Republic of China, by NewsRx journalists, research stated, "High entropy alloys (HEAs) have excellent properties because they can form simple solid solution (SS) phases, including body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, or FCC + BCC phase, so phase prediction is the first step in alloy design." Financial supporters for this research include Joint Fund of The National Natural Science Foundation of China And The Karst Science Research Center of Guizhou Province. The news editors obtained a quote from the research from Chengdu University: "In current research, machine learning (ML) approach had been widely used to guide the discovery and design of materials. The prediction of HEAs phase structure based on machine learning (ML) is a hot topic. In this work, five ML algorithms were utilized to predict HEAs for SS and amorphous (AM) phases based on 399 collected data sets, including 120 BCC alloys, 87 FCC alloys, 82 BCC + FCC alloys and 110 a.m. alloys. To enhance the model's accuracy, grid search and K-fold cross validation were used to optimize performance. Valence electron concentration (VEC) and DHmix exhibit high importance in prediction in compared to other parameters. The results show that the random forest can effectively distinguish BCC phase, FCC phase, mixed solid solution phase (FCC + BCC) and AM, with an accuracy is 0.87."
Chengdu UniversityChengduPeople's Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
