首页|New Machine Learning Research from Shanghai Jiao Tong University Described (Mach ine Learning Design for High-Entropy Alloys: Models and Algorithms)

New Machine Learning Research from Shanghai Jiao Tong University Described (Mach ine Learning Design for High-Entropy Alloys: Models and Algorithms)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Shanghai, People's Re public of China, by NewsRx correspondents, research stated, "High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design." Financial supporters for this research include Science And Technology Cooperatio n Project of Inner Mongolia Autonomous Region And Shanghai Jiao Tong University. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University: "However, obtaining HEAs with low density and high properties throug h experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HE As, the accuracy of prediction is limited owing to the indirect correlation betw een the theoretical calculation values and performances. Recently, machine learn ing (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design." According to the news reporters, the research concluded: "The advantages and lim itations of these ML models and algorithms are analyzed and their potential weak nesses and corresponding optimization strategies are discussed as well. This rev iew suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future H EA design."

Shanghai Jiao Tong UniversityShanghaiPeople's Republic of ChinaAsiaAlgorithmsAlloysCyborgsEmerging Technol ogiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.6)