Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2-Reference-Cited by-同舟云学术

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Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2

Published:2024-08 Issue: Volume:296 Page:120280
ISSN:0009-2509
Container-title:Chemical Engineering Science
language:en
Short-container-title:Chemical Engineering Science

Author:

Peng Xuan^ORCID,Wang Hao

Publisher

Elsevier BV

Reference81 articles.

1. Opportunities and challenges of MOF-based membranes in gas separations;Adatoz;Sep. Purif. Technol.,2015

2. Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning;Anderson;Chem. Mater.,2018

3. Metal-organic frameworks in heterogeneous catalysis: recent progress, new trends, and future perspectives;Bavykina;Chem. Rev.,2020

4. High-throughput screening of metal-organic frameworks for hydrogen storage at cryogenic temperature;Bobbitt;J. Phys. Chem. C,2016

5. Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage;Bobbitt;Mol. Simul.,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning for the advancement of membrane science and technology: A critical review;Journal of Membrane Science;2025-01

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