首页|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)
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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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.”
ChengduPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysicsXihua University
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