Data-knowledge dual-driven intelligent framework for full-chain, experiment-efficient synthesis of 2D dendrites
机器智能支持二维树突合成的全流程
机构 * School of Physics, Hunan Key Laboratory for Super-Microstructure and Ultrafast Process, and Hunan Key Laboratory of Nanophotonics and Devices, Central South University(物理学院,湖南超级微结构与超快工艺重点实验室,湖南纳米光子学与器件重点实验室,中南大学) ; College of Aerospace Science and Engineering, National University of Defense Technology(航空科学与工程学院,国防科技大学) ; School of Advanced Materials, Guangdong Provincial Key Laboratory of Nano-Micro Materials Research, Peking University Shenzhen Graduate School(先进材料学院,广东省纳米-微米材料研究重点实验室,北京大学深圳研究生院) ; College of Science, National University of Defense Technology(科学学院,国防科技大学) ; School of Physics and Technology, and Xinjiang Key Laboratory of Solid-State Physics and Devices, Xinjiang University(物理与技术学院,新疆固态物理与器件重点实验室,新疆大学) ; State Key Laboratory of Powder Metallurgy, and Powder Metallurgy Research Institute, Central South University(粉末冶金国家重点实验室,中南大学粉末冶金研究所)
专题命中 工作流自动化 :workflow(abstract);分类 cs.AI
AI总结 本文提出基于机器学习的材料合成全流程框架,通过主动学习优化实验参数,结合数据增强策略提升预测精度,并构建双驱动机制模型揭示多参数对产物形态的协同作用。
Comments 57 pages, 30 figures
Journal ref Science Bulletin (2026)