化工学报2025，Vol.76Issue(8)：4259-4272,14.DOI:10.11949/0438-1157.20250178
数据驱动辅助高通量筛选阴离子柱撑金属有机框架储氢
Data-driven high-throughput screening of anion-pillared metal-organic frameworks for hydrogen storage
摘要
Abstract
Hydrogen storage is a core issue that hinders the development of the hydrogen energy industry,and improving hydrogen storage density is a key technical difficulty.Metal-organic frameworks(MOFs)exhibit excellent hydrogen storage density,and have become one of the most promising energy storage materials.However,conventional computational approaches,including traditional molecular simulations and high-throughput screening methods,encounter significant limitations when applied to MOF hydrogen storage research.These methods are particularly constrained by their excessive computational time requirements and substantial resource consumption,which hinder efficient material discovery and optimization.To address these challenges,this study developed a data-driven high-throughput screening strategy for the rapid prediction of hydrogen storage performance in anion-templated metal-organic frameworks(AP-MOFs).The proposed method achieved exceptional predictive accuracy,with R2 values exceeding 0.99 for both the training and test sets,and required only 63 s of computation time.Through this approach,20 AP-MOFs with a hydrogen storage density exceeding 5.5％(mass)at 77 K and 5 MPa were identified,surpassing the hydrogen storage target set by the United States Department of Energy.Among these,the ALFFIVE_2_Fe structure,which is potentially synthesizable,exhibited a remarkable hydrogen storage density of 9.75％(mass)under the same conditions,along with a deliverable hydrogen storage density of 3.05％(mass).The study also revealed that the volumetric porosity has the greatest impact on hydrogen storage performance,followed by gravimetric surface area,density and porosity.These findings provide theoretical insights for the future application of AP-MOFs in hydrogen storage technologies.关键词
金属有机框架/高通量筛选/机器学习/储氢/分子模拟Key words
metal-organic frameworks/high-throughput screening/machine learning/hydrogen storage/molecular simulation分类
化学化工引用本文复制引用
高正,汪辉,屈治国..数据驱动辅助高通量筛选阴离子柱撑金属有机框架储氢[J].化工学报,2025,76(8):4259-4272,14.基金项目
国家重点研发计划项目(2023YFB4005404) （2023YFB4005404）
国家自然科学基金项目(52176088) （52176088）
跨域飞行交叉技术实验室项目(2024-KF01004) （2024-KF01004）
