首页|Study Results from Peking University Broaden Understanding of Machine Learning ( Machine Learning-driven Discovery and Structure-activity Relationship Analysis of Conductive Metalorganic Frameworks)
Study Results from Peking University Broaden Understanding of Machine Learning ( Machine Learning-driven Discovery and Structure-activity Relationship Analysis of Conductive Metalorganic Frameworks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting from Beijing, People's Republic of C hina, by NewsRx journalists, research stated, “Electrically conductive metal-org anic frameworks (MOFs) are a class of materials with emergent applications in fi elds such as electrocatalysis, electrochemical energy storage, and chemiresistiv e sensors due to their unique combination of porosity and conductivity. However, due to the structural complexity and versatility, rational design of conductive MOFs is still challenging, which limits their further development and applications.”
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningPeking University
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