首页|Tsinghua University Reports Findings in Machine Learning (Materials descriptors of machine learning to boost development of lithiumion batteries)
Tsinghua University Reports Findings in Machine Learning (Materials descriptors of machine learning to boost development of lithiumion batteries)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "Traditional methods for deve loping new materials are no longer sufficient to meet the needs of the human ene rgy transition. Machine learning (ML) artificial intelligence (AI) and advanceme nts have caused materials scientists to realize that using AI/ML to accelerate t he development of new materials for batteries is a powerful potential tool." Funders for this research include National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China. Our news journalists obtained a quote from the research from Tsinghua University, "Although the use of certain fixed properties of materials as descriptors to a ct as a bridge between the two separate disciplines of AI and materials chemistr y has been widely investigated, many of the descriptors lack universality and ac curacy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning log ic of AI/ML has become mandatory for materials scientists to develop more accura te descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic o f AI and machine learning to help materials developers understand their operatio nal mechanisms."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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