首页|Findings from Chinese Academy of Sciences Broaden Understanding of Machine Learning (Navigating Materials Chemical Space To Discover New Battery Electrodes Using Machine Learning)
Findings from Chinese Academy of Sciences Broaden Understanding of Machine Learning (Navigating Materials Chemical Space To Discover New Battery Electrodes Using Machine Learning)
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NETL
NSTL
Elsevier
Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “Investigating the role of electrodes’ physiochemical properties on their output voltage can be beneficial in developing high-performance batteries. To this end, this study uses a two-step machine learning (ML) approach to predict new electrodes and analyze the effects of their physiochemical properties on the voltage.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province, Shenzhen Science and Technology Program, Shenzhen Science and Technology Program, Shenzhen-Hong Kong -Macau Technology Research Program, Shenzhen Excellent Science and Technology Innovation Talent Training Project-Outstanding Youth Project, CCFTencent Open Fund, Iwatani Naoji Foundation. The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The first step utilizes an ML model to curate an informative feature space that elucidates the relationship between physiochemical properties and voltage output. The second step trains an active learning model on the informative feature space using Bayesian optimization to screen potential battery electrodes from a dataset of 3656 materials. This strategy successfully identified 41 electrode materials that exhibit good electronic conductivity and host highly electronegative anions.”
GuangdongPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningChinese Academy of Sciences
NETL
NSTL
Elsevier
