首页|Findings from Beihang University Provides New Data on Machine Learning (Potentialmind: Graph Convolutional Machine Learning Potential for Sb-te Binary Compounds of Multiple Stoichiometries)
Findings from Beihang University Provides New Data on Machine Learning (Potentialmind: Graph Convolutional Machine Learning Potential for Sb-te Binary Compounds of Multiple Stoichiometries)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Research findings on Machine Learning are discussed in a new report. According to news reportingout of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Machine learning potential(MLP) has emerged as a powerful tool in materials research and design. However, most MLP methods relyonly on a single descriptor generated by mathematical functions instead of mapping the three-dimensionalspace of the materials structure, and thus this type of potential is typically limited to specific compositions.”Funders for this research include National Natural Science Foundation of China (NSFC), National KeyResearch and Development Program of China, National Natural Science Foundation of China (NSFC),China Postdoctoral Science Foundation.
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysicsBeihang University
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