Evaluating CH
adsorption
gas separation
machine learning
metal−organic framework
molecular simulation
Journal
ACS applied materials & interfaces
ISSN: 1944-8252
Titre abrégé: ACS Appl Mater Interfaces
Pays: United States
ID NLM: 101504991
Informations de publication
Date de publication:
11 Dec 2023
11 Dec 2023
Historique:
medline:
12
12
2023
pubmed:
12
12
2023
entrez:
12
12
2023
Statut:
aheadofprint
Résumé
Considering the large abundance and diversity of metal-organic frameworks (MOFs), evaluating the gas adsorption and separation performance of the entire MOF material space using solely experimental techniques or brute-force computer simulations is impractical. In this study, we integrated high-throughput molecular simulations with machine learning (ML) to explore the potential of both synthesized, the real MOFs, and computer-generated, the hypothetical MOFs (hypoMOFs), for adsorption-based CH
Identifiants
pubmed: 38082488
doi: 10.1021/acsami.3c13533
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM