Green Chemical Engineering（GreenChE）第6卷第2期已上线，本期为“AI-assisted Chemical Engineering”专刊，由华东理工大学漆志文教授、重庆大学申威峰教授、华东理工大学宋震教授和墨西哥Universidad Michoacana de San Nicolás de Hidalgo大学José María Ponce Ortega教授共同担任客座编辑，共收录文章13篇，其中包括Editorial 1 篇，Review 1 篇，Article 11 篇。目前此专刊已正式在ScienceDirect上线，敬请参阅！

封面故事

本期封面展示了华东理工大学漆志文教授团队的研究工作。封面以神经网络和离子液体结构为背景，突出人工智能和材料学科交叉的特点。封面中央展示了离子液体和金属有机框架复合材料（IL@MOF）在CO₂/N₂分离领域的应用，其下方围绕着机器学习相关的算法模型、学习曲线以及模型结果图像，而从左到右的过渡象征着人工智能将在更多材料设计领域发挥重要作用。

本文结合分子模拟和机器学习实现了IL@MOF复杂材料CO₂/N₂吸附分离性能的准确预测。通过结构-性能分析表明，采用选择性与工作容量双重指标筛选吸附剂更为合理；通过调整离子液体负载量可在选择性与工作容量之间实现良好的平衡。该研究不仅筛选出了高性能的复合物结构，还为CO₂/N₂分离吸附剂设计提供了一定的理论指导。

文章信息（点击文章题目查看详情）

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