首页|Data from University of Virginia Broaden Understanding of Machine Learning (Pred icting Yield Strength and Plastic Elongation in Body- Centered Cubic High-Entropy Alloys)
Data from University of Virginia Broaden Understanding of Machine Learning (Pred icting Yield Strength and Plastic Elongation in Body- Centered Cubic High-Entropy Alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Charlottesville, Virg inia, by NewsRx editors, the research stated, “We employ machine learning (ML) t o predict the yield stress and plastic strain of body-centered cubic (BCC) high- entropy alloys (HEAs) in the compression test.” Funders for this research include University of Virginia Department of Physics F ellowship. Our news journalists obtained a quote from the research from University of Virgi nia: “Our machine learning model leverages currently available databases of BCC and BCC+B2 entropy alloys, using feature engineering to capture electronic facto rs, atomic ordering from mixing enthalpy, and the D parameter related to stackin g fault energy. The model achieves low Root Mean Square Errors (RMSE). Utilizing Random Forest Regression (RFR) and Genetic Algorithms for feature selection, ou r model excels in both predictive accuracy and interpretability. Rigorous 10-fol d cross-validation ensures robust generalization.”
University of VirginiaCharlottesvilleVirginiaUnited StatesNorth and Central AmericaAlloysCyborgsEmerging T echnologiesMachine Learning
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