ArticleACS applied materials & interfaces2023
Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF
Article in ACS applied materials & interfaces, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends.Molecules (Basel, Switzerland) · 2026Review
- Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Accelerated Discovery of Metal-Organic Frameworks for COIndustrial & engineering chemistry research · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.