arXiv:2608. 01431v1 Announce Type: cross Abstract: Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design.
By Charlie Pyle, Adarsh Gadari, C. Adrian Figg, Zhenquan Jia, Yaohang Li, Chunjiang Zhu
arXiv:2606. 20753v2 Announce Type: replace-cross Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing.
By Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, Rui Su, Gang Wang, Muyao Yu, Dong Zhong, Jie Zhu, Weibo Gong, Huan Gu, Limin Li, Chen Shen, Rui Wu, Zhenghao Wu, Kan Xu, Min Zhou, Donglin He, Xiayun Huang, Shan Jiang, Pengfei Ou, Jiayu Peng, Yuwei Zhang, Jie Zhao, Di Zhang, Piao Ma, Zhenghao Li, Hao Li
arXiv:2510. 16023v2 Announce Type: replace Abstract: Linear polymers, macromolecules formed from monomers covalently bonded into continuous chains, underpin countless technologies and are indispensable to modern life.
By Fanmeng Wang, Ruochao Wang, Shan Mei, Wentao Guo, Hongshuai Wang, Qi Ou, Zhifeng Gao, Hongteng Xu
arXiv:2607. 29256v1 Announce Type: new Abstract: Designing polyimide structures with specific glass transition temperatures (Tg) is highly challenging.
By Junquan Hu, Zhihui Wang, Peng Xu, Xinru Guo, Xintong Li, Kun Lu, Ben Fei
arXiv:2605. 26833v2 Announce Type: replace-cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.
By Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto, Kelin Xia
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.
By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese