Article detail · 2021
Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
- Year
- 2021
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue JOURNAL OF CHEMICAL INFORMATION AND MODELING
- Catalog match (ISSN) Journal of Chemical Information and Modeling
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
240 citations
OpenAlex cited_by_count (cache / database)
7 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Recent advances in computational modeling of MOFs: From molecular simulations to machine learning 2023
- Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs 2022
- Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research 2025
- On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification 2023
- Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning 2023
- Data‐Driven Design and Discovery of Metal–Organic Framework/Polymer Mixed Matrix Membranes 2025
- The transformative role of machine learning in advancing MOF membranes for gas separations 2025