Papers citing Machine learning enables polymer cloud-point engineering via inverse design
Amphiphilic Hollow Microgels at the Liquid–Liquid Interface: Studying Soft Colloids’ Shape and Interior via Computer Simulations and Machine Learning
Langmuir · 2026
Sofiya S. Kudryashova, Rustam A. Gumerov, Mikhail V. Anakhov, Viktor A. Nemchenko, Igor I. Potemkin
Read original paper ↗ ·Advanced representations and generative artificial intelligence for polyolefins
Clean Energy · 2026
Jiayi Song, Shan Ye, Rongjuan Cong, Shoupeng Qiao, Linge Ma, Shuai Shao
Read original paper ↗ ·AI-driven EcoBlend optimiser: Machine learning ranking of feedstock oils for biodiesel suitability
Bioresource Technology Reports · 2026
R. Sudharsanan, K. Sathish Kumar, Punniyakotti Varadharajan Gopirajan, Gurunathan Baskar
Read original paper ↗ ·AI-Empowered Separation Membranes: Challenges and Opportunities
Engineering · 2026
Yanan Guo, Gongping Liu, Wanqin Jin
Read original paper ↗ ·Artificial intelligence in nanotechnology: current applications and future perspectives
Health Nanotechnology · 2026
Ali Majedi
Read original paper ↗ ·Cross-Scale Machine Learning for Polymer Materials: Linking Molecular Structure, Mesoscale Organization, Processing History, and Macroscopic Properties
ACS Applied Polymer Materials · 2026
Yijun Yao, Mingyong Zhu, Qinhan Chen, Yanlong Luo
Read original paper ↗ ·Engineering Smart Hydrogels Through Dynamic Polymer Networks for Controlled Drug Delivery
Gels · 2026
Chanju Choi, Dongseong Seo, Taeho Kim, Jonghyun Park, Dongmin Yu, Sohyeon Yu, Jeongmin Shin, Simseok A. Yuk, Daekyung Sung, Hyungjun Kim
Read original paper ↗ ·Polymer informatics for decision-making in industrial R&D: Challenges, opportunities, and perspectives
MRS Bulletin · 2026
拓也 南, Katsuhisa Yoshida
Read original paper ↗ ·