Jing Lin

Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Jing Lin, Zhimeng Liu, Yujie Guo et al. · Nano Today · 2023 · 76 citations

Artificial Intelligence, Design Space Exploration, Data-driven Optimization +9

Electronic state, abundance and microenvironment modulation of Ru nanoclusters within hierarchically porous UiO-66(Ce) for efficient hydrogenation of dicyclopentadiene

Rushuo Li, Linmeng Wang, Peiyun Zhou et al. · CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) · 2024 · 33 citations

Materials Science, Engineering, Nanocluster +10

Machine‐learning‐assisted intelligent synthesis of UiO‐66(Ce): Balancing the trade‐off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene

Jing Lin, Tao Ban, Li Tian et al. · Materials Genome Engineering Advances · 2024 · 29 citations · Full text

Materials Science, Chemical Engineering, Novel Organocatalysts +13

Defect engineering and Ni promoter synergistically accelerating electron transfer to Ru0 sites in UiO-66(Ce) for dicyclopentadiene hydrogenation under mild condition

Rushuo Li, Tao Ban, Danfeng Zhao et al. · Nano Research · 2024 · 27 citations

Electrostatic potential-derived charge: a universal OER performance descriptor for MOFs

Xiangdong Xue, Hongyi Gao, Jiangtao Liu et al. · Chemical Science · 2022 · 22 citations · Full text

Engineering, Functional Mofs, Computational Chemistry +20