| Editorial |
| Artificial intelligence for chemical engineering |
| Zhen Song*, Weifeng Shen*, Zhiwen Qi*, José María Ponce Ortega* PP. 137-138 |
| Review |
| Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review |
| Sijing Wang, Ruoyu Zhou, Yijia Ren, Meiyuan Jiao, Honglai Liu, Cheng Lian* PP. 139-153 |
 |
| Articles |
| Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production |
| Qingchun Yang*, Lei Zhao, Jingxuan Xiao, Rongdong Wen, Fu Zhang, Dawei Zhang* PP. 154-168 |
 |
| Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production |
| Zhibo Zhang1, Yaowei Wang1, Dongrui Zhang, Deming Zhao, Huibin Shi, Hao Yan, Xin Zhou*, Xiang Feng*, Chaohe Yang PP. 169-180 |
 |
| Developing deep learning-based large-scale organic reaction classification model via sigma-profiles |
| Wenlong Wang, Chenyang Xu, Jian Du, Lei Zhang* PP. 181-192 |
 |
| Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms |
| Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong*, Jaka Sunarso, Weifeng Shen* PP. 193-199 |
 |
| Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture |
| Mengjia Sheng, Xiang Zhang*, Hongye Cheng, Zhen Song, Zhiwen Qi* PP. 200-208 |
 |
| Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure |
| Yixiong Lin, Zhengqi Wu, Shiqi You, Chen Yang*, Qinglian Wang, Wang Yin, Ting Qiu* PP. 209-222 |
 |
| Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids |
| Kaikai Li, Yuesong Zhu, Sensen Shi, Yongzheng Song, Haiyan Jiang, Xiaochun Zhang, Shaojuan Zeng, Xiangping Zhang* PP. 223-232 |
 |
| Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data |
| Zhiqiang Wu1, Yunquan Chen1, Bingjian Zhang, Jingzheng Ren, Qinglin Chen, Huan Wang*, Chang He* PP. 233-248 |
 |
| Evaluating ionic liquid toxicity with machine learning and structural similarity methods |
| Rongli Shan1, Runqi Zhang1, Ying Gao, Wenxin Wang, Wenguang Zhu, Leilei Xin, Tianxiong Liu, Yinglong Wang*, Peizhe Cui PP. 249-262 |
 |
| COSMO-RS screening of organic mixtures for membrane extraction of aromatic amines: TOPO-based mixtures as promising solvents |
| Gilles Van Eygen*, Catherine Echezuria, Anita Buekenhoudt, João A.P. Coutinho, Bart Van der Bruggen, Patricia Luis PP. 263-274 |
 |
| Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution |
| Mood Mohan*, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder*, Jeremy C. Smith* PP. 275-287 |
 |
