Robotics & Machine Learning Daily News2024,Issue(Jan.11) :37-38.

Reports Outline Machine Learning Study Findings from Xihua University (Composition Optimization of Alfecusimg Alloys Based On Elastic Modules: a Combination Method of Machine Learning and Molecular Dynamics Simulation)

Robotics & Machine Learning Daily News2024,Issue(Jan.11) :37-38.

Reports Outline Machine Learning Study Findings from Xihua University (Composition Optimization of Alfecusimg Alloys Based On Elastic Modules: a Combination Method of Machine Learning and Molecular Dynamics Simulation)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Fresh data on Machine Learning are presented in a new report. According to news reporting outof Chengdu, People’s Republic of China, by NewsRx editors, research stated, “High entropy alloys (HEAs)has attracted much attention owning to its excellent mechanical properties. However, the application ofthese HEAs is limited by the uncertain element ratio, high cost and low efficiency preparation methods.”Our news journalists obtained a quote from the research from Xihua University, “In this work, weoptimize the composition of AlFeCuSiMg HEAs based on elastic modulus as a prediction index throughmachine learning (ML) and molecular dynamics (MDs) simulation. The training sets and test sets areprepared by MDs. By comparing the average R2 and RMSE values of different ML models, we selectedsupport vector regression (SVR) model and random forest (RF) regression model to predict the elasticmodulus of AlFeCuSiMg HEAs.”

Key words

Chengdu/People’s Republic of China/Asia/Alloys/Cyborgs/Emerging Technologies/Machine Learning/Molecular Dynamics/Physics/Xihua University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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