Liang Gao, Jiaping Lin*, Liquan Wang, and Lei Du

Machine Learning-Assisted Design of Advanced Polymeric Materials

Acc. Mater. Res. 2024, 5, 571-584

Chunhua Cai, Hongfeng Tang, Feiyan Li, Zhanwen Xu*, Jiaping Lin*, Da Li, Zhengmin Tang, Chunming Yang, and Liang Gao

Archimedean Spirals with Controllable Chirality: Disk Substrate-Mediated Solution Assembly of Rod–Coil Block Copolymers

JACS Au 2024, 4, 2363-2371

Guomei Zhao^#, Tianhao Xu^#, Xuemeng Fu, Wenlin Zhao, Liquan Wang*, Jiaping Lin*, Yaxi Hu, Lei Du

Machine-learning-assisted multiscale modeling strategy for predicting mechanical properties of carbon fiber reinforced polymers

Compos. Sci. Technol. 2024, 248, 110455

Wenlin Zhao#, Xuemeng Fu#, Xinyao Xu, Liangshun Zhang*, Liquan Wang*, Jiaping Lin*, Yaxi Hu, Liang Gao, Lei Du, Xiaohui Tian

Design of multicomponent thermosetting polymers with enhanced tensile properties through active learning

Compos. Sci. Technol. 2024, 256, 110779

Qipeng Song, Pengchao Wu, Fan Liu, Zichao Sun, Caixia Jiang, Liang Gao, Jianzhuang Chen, Haibao Jin*, Jiaping Lin*, and Shaoliang Lin*

Ultrathin Polymer Nanotubes Assembled from Side-Chain Amphiphilic Alternating Azocopolymers for the Potential of Highly-Efficient and Photo-Controllable Dye Removal

Macromolecules 2024, 57, 5892-5901

Kexin Cao, Yue Du, Jiaping Lin*, Chunhua Cai*

Regulating the Chirality of the Superhelices Self-Assembled from Block Copolymer/Homopolymer Binary Systems by the Mixing Ratio of the Polymers

Macromolecules 2024, 57, 6514-6521

Tao Zeng, Jiaping Lin, Liangshun Zhang*

Enhancement of Mechanical Performance of Nanostructured Materials by Architectural Design of Pentablock Terpolymers

Macromolecules 2024, DOI: 10.1021/acs.macromol.4c01043

Liang Gao^#, Zhengmin Tang^#, Jiaping Lin, Chunhua Cai, and Gerald Guerin

Living Growth Kinetics of Polymeric Micelles on a Substrate

Langmuir 2024, 40, 9613-9621

Shuang Song#, Xinyao Xu#, Haoxiang Lan, Liang Gao*, Jiaping Lin*, Lei Du, Yuyuan Wang

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach

Macromol. Rapid Commun. 2024, DOI: 10.1002/marc.202400337