首页|Shenzhen Research Institute of Shandong University Researchers Detail New Studies and Findings in the Area of Machine Learning (Machine learning assisted design of high-entropy alloys with ultrahigh microhardness and unexpected low density)
Shenzhen Research Institute of Shandong University Researchers Detail New Studies and Findings in the Area of Machine Learning (Machine learning assisted design of high-entropy alloys with ultrahigh microhardness and unexpected low density)
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NETL
NSTL
Elsevier
Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “High-entropy alloys (HEAs) have attracted considerable attention for their exceptional microstructures and properties. Discovering new HEAs with desirable properties is crucial, but traditional design methods are laborious and time-consuming.” Funders for this research include Shenzhen Fundamental Research Program. The news reporters obtained a quote from the research from Shenzhen Research Institute of Shandong University: “Fortunately, the emerging Machine Learning (ML) offers an efficient solution. In this study, composition-microhardness data pairs from various alloy systems were collected and expanded using a Generative Adversarial Network (GAN). These data pairs were converted into empirical parametermicrohardness pairs. Then Active Learning (AL) was employed to screen the Al-Co-Cr-Cu-Fe-Ni system and identify the eXtreme Gradient Boosting (XGBoost) as the optimal ML master model. Millions of data training iterations employing the XGBoost sub-model and accuracy evaluations using the Expected Improvement (EI) algorithm establish the relationship between HEA compositions and microhardness. The proposed sub-model aligns well with experimental data, wherein four Al-rich compositions exhibit ultra-high microhardness (>740 HV, with a maximum of 780.3 HV) and low density (<5.9 g/cm3) in the as-cast bulk state.” According to the news editors, the research concluded: “The hardening increment originates from the precipitation of disordered BCC nanoparticles in the ordered AlCo-rich B2 matrix compared to the dilute B2 AlCo intermetallics. This lightweight, high-performance alloy shows potential for engineering applications as thin films or coatings.”
Shenzhen Research Institute of Shandong UniversityShenzhenPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
