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机译:通过机器学习和反应性描述符加速酶催化合成条件的优化
Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization Low Carbon Energy Institute and School of Chemical Engineering China University of Mining and Technology Xuzhou 221008 People's Republic of China School of Science City University of Hong Kong Hong Kong SAR 999077 People's Republic of China;
Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization Low Carbon Energy Institute and School of Chemical Engineering China University of Mining and Technology Xuzhou 221008 People's Republic of China;
School of Science Xi'an Polytechnic University Xi'an 710048 People's Republic of China Department of Physics Sungkyunkwan University Suwon 16419 Korea;
School of Environment and Safety Engineering North University of China Taiyuan 030051 People's Republic of China;
机译:通过传递强化学习来加速生物启发式优化器,实现无功优化
机译:优化多体原子描述符以增强基于机器学习的原子间电势的计算性能
机译:优化许多身体原子描述符,提高基于机器的基于机器的内部电位的计算性能
机译:反应性模板生长法优化织构化(Bi_(0.5)K_(0.5))TiO_3压电陶瓷的合成条件
机译:通过利用功能生长条件加速机器学习中的凸优化
机译:机器学习应用程序和聚类方法的优化改进了黑莓种质库中的描述符选择
机译:通过利用功能生长条件加速机器学习中的凸优化