five

A Systematic Approach for Incorporating Structural Flexibility in High-Throughput Computational Screening of Metal–Organic Frameworks for Xylene Separation

收藏
Figshare2025-03-28 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/A_Systematic_Approach_for_Incorporating_Structural_Flexibility_in_High-Throughput_Computational_Screening_of_Metal_Organic_Frameworks_for_Xylene_Separation/28685357
下载链接
链接失效反馈
官方服务:
资源简介:
Separation of xylene isomers poses a significant challenge due to their similar physicochemical properties. Currently, zeolites are utilized as adsorbents for this purpose in the chemical industry despite suboptimal separation performance. With tunable pore size and chemical functionality, metal–organic frameworks (MOFs) are promising adsorbents for separation. By virtue of high-throughput computational screening (HTCS), the adsorption performance of a large collection of MOFs can be evaluated in silico by using Monte Carlo (MC) simulations. Unlike prior studies assuming rigid structures of MOFs during screening, we develop a systematic approach for incorporating flexibility in HTCS for xylene separation. First, MOFs are judiciously classified with external flexibility (volume/shape changes) and internal flexibility (intraframework fluctuations), respectively, based on the nature and extent of structural deformation from molecular dynamics (MD) simulations. Afterward, adsorption in MOFs with external flexibility is simulated by hybrid MC/MD method, the flexible snapshot method is used for MOFs with a sort of internal flexibility, and grand-canonical MC (GCMC) method is employed for MOFs with negligible flexibility. Finally, top-performing MOFs are identified for xylene separation. While substantially reducing computational cost, this study also delivers more reliable results compared to the assumption of rigid structures. The HTCS approach is versatile and can be extended beyond MOFs, offering a robust tool for the virtual screening of other soft-porous materials for a wide range of important applications.
创建时间:
2025-03-28
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作