ReviewChemical science2021
A review on machine learning algorithms for the ionic liquid chemical space.
Review in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Machine Learning-Driven Discovery of Sustainable Ionic Liquids for COThe journal of physical chemistry. B · 2026Article
- Identifying the impact of chemical functional groups on ionic liquid conductivity.Chemical science · 2026Article
- Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends.Molecules (Basel, Switzerland) · 2026Review
- The Role of Ionic Liquids at the Biological Interfaces in Bioelectronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Uncertainty Quantification forChemical reviews · 2026Review
- Protic Ionic Liquid-HThe journal of physical chemistry. B · 2026Article
- General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.Molecular diversity · 2025Article
- Revealing the Nature of Non-Covalent Interactions in Ionic Liquids by Combined Pulse EPR andAngewandte Chemie (International ed. in English) · 2025Article
- Prediction of Melting Points of Chemicals with a Data Augmentation-Based Neural Network Approach.ACS omega · 2025Article
- Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution.Journal of cheminformatics · 2025Article
- Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies.Chem & bio engineering · 2025Review
- Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.Journal of chemical information and modeling · 2025Review
- Intelligent design and synthesis of energy catalytic materials.Fundamental research · 2025Review
- Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma.Journal of translational medicine · 2024Article
- A comprehensive dataset on cytotoxicity of ionic liquids.Scientific data · 2024Article
- Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches.Molecules (Basel, Switzerland) · 2024Review
- Planting the Seeds of a Decision Tree for Ionic Liquids: Steric and Electronic Impacts on Melting Points of Triarylphosponium Ionic Liquids.The journal of physical chemistry. B · 2024Article
- Are nature's strategies the solutions to the rational design of low-melting, lipophilic ionic liquids?Chemical communications (Cambridge, England) · 2024Review
- Process Simulation and Optimization on Ionic Liquids.Chemical reviews · 2024Review
- Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.
Identifiers
What OpenQuestion holds
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.