首页|Study Findings from Zhengzhou University Broaden Understanding of Machine Learning (Accelerating the Design of High-entropy Alloys With High Hardness By Machine Learning Based On Particle Swarm Optimization)
Study Findings from Zhengzhou University Broaden Understanding of Machine Learning (Accelerating the Design of High-entropy Alloys With High Hardness By Machine Learning Based On Particle Swarm Optimization)
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NETL
NSTL
Elsevier
New research on Machine Learning is the subject of a report. According to news originating from Zhengzhou, People's Republic of China, by NewsRx correspondents, research stated, "Combining a machine-learning (ML) surrogate model with a particle swarm optimization (PSO) algorithm, we propose a design strategy to search for high-entropy alloys (HEAs) with high hardness in the Al-Co-Cr- Cu-Fe-Ni system. The relationship between various materials descriptors and a targeted property can be established by the ML models based on the data set, which contains the mole fraction of each element." Financial supporters for this research include Key Scientific and Technological Project of Henan Province, Strategic Research and Consulting Project of Chinese Academy of Engineering, National Natural Science Foundation of China (NSFC), Zhong Yuan Science and Technology Innovation Leadership Program, National Science Foundation (NSF), Army Research Office Project.
ZhengzhouPeople's Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningParticle Swarm OptimizationZhengzhou University
NETL
NSTL
Elsevier
