摘要
吸附剂研究是吸附分离研究的核心,加速新型吸附分离技术发展的关键在于多孔吸附剂的筛选。金属有机框架等新型多孔材料在吸附分离领域受到了广泛关注,近年其数量呈爆炸式增长,但这也给吸附剂筛选带来了压力。机器学习引领了多孔材料在发现、设计和应用上的创新突破,正推动多孔吸附剂研究进入数据驱动的全新范式。本文介绍了近年来机器学习在多孔吸附剂领域的研究现状,通过关键案例研究梳理了多孔材料数据库、吸附性能预测及其他相关机器学习任务上的进展,分析了在多孔材料机器学习中模型输入的原理和特点。最后总结出标准化数据库、促进知识迁移、弥合实验与模拟数据的差异及可解释模型是未来多孔吸附剂机器学习研究的发展方向。本文为使用机器学习开发新型多孔吸附剂的研究者提供了简明的资源。
Adsorbent research is crucial in the field of adsorption and separation,so the key to accelerating the development of new adsorption separation technology lies in the screening of porous adsorbents.New porous materials of metal-organic frameworks have received widespread attention in the field of adsorption and separation.The number of them has exploded in recent years,but it has also brought pressure to the screening of adsorbents.Machine learning has brought innovative breakthroughs in the discovery,design and application of porous materials,leading the research of porous adsorbents into a new data-driven paradigm.This article introduced the current status of machine learning research in the field of porous adsorbents in recent years.Through key case studies,it sorted out the progress in the database of porous materials,adsorption performance prediction and other related machine learning works,and analyzed the principles and characteristics of model input in porous material machine learning.Finally,it was concluded that standardized databases,knowledge transfer,bridging the gap between experimental and simulation data and interpretable models were the future development directions of machine learning research on porous adsorbents.The article provided concise resources for researchers who wanted to use machine learning to develop new porous adsorbents.
作者
杨证禄
杨立峰
路晓飞
锁显
张安运
崔希利
邢华斌
YANG Zhenglu;YANG Lifeng;LU Xiaofei;SUO Xian;ZHANG Anyun;CUI Xili;XING Huabin(College of Chemical and Biological Engineering,Zhejiang University,Hangzhou 310058,Zhejiang,China;Hangzhou Global Scientific and Technological Innovation Center,Zhejiang University,Hangzhou 311215,Zhejiang,China)
出处
《化工进展》
北大核心
2025年第8期4288-4301,共14页
Chemical Industry and Engineering Progress
基金
国家自然科学基金(22227812,22438011)
浙江省自然科学基金(LD24B010001)。
关键词
吸附剂
多孔材料
机器学习
数据驱动
预测
分离
算法
adsorbent
porous materials
machine learning
data-driven
prediction
separation
algorithm
