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Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points?
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A machine learning workflow for molecular analysis: application to melting points
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Determination of Water Content in Automobile Lubricant Using Near-Infrared Spectroscopy Improved by Machine Learning Analysis
inferred← citeshttps://doi.org/10.1134/s0012500810020023openalex-snapshot
Pseudofragmental descriptors based on combinations of atomic properties for prediction of physical properties of polymers in quantitative structure—property relationship studies
inferred← citeshttps://doi.org/10.1186/s13321-016-0113-yopenalex-snapshot
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS
inferred← citeshttps://doi.org/10.1524/ract.2009.1617openalex-snapshot
Actinide and lanthanide speciation in imidazolium-based ionic liquids
inferred← citeshttps://doi.org/10.2174/9781608057450113010005openalex-snapshot
Building a Chemical Space Based on Fragment Descriptors
inferred← citeshttps://doi.org/10.25177/jccmm.5.1.ra.10752openalex-snapshot
Quantum and Structural Molecular Fragment models used to predict anti-inflammatory activity
inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-03m5qopenalex-snapshot
Predicting Propellant Properties of Boron-Based Hypergolic Ionic Liquids via Machine Learning
inferred← citeshttps://doi.org/10.3389/fenrg.2021.609070openalex-snapshot
A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts
inferred← citeshttps://doi.org/10.3390/app12052408openalex-snapshot
Machine-Learning Model Prediction of Ionic Liquids Melting Points
inferred← citeshttps://doi.org/10.3390/app9245367openalex-snapshot
Predicting Melting Points of Biofriendly Choline-Based Ionic Liquids with Molecular Dynamics
inferred← citeshttps://doi.org/10.3390/chemistry6060094openalex-snapshot
Rapid and Accurate Prediction of the Melting Point for Imidazolium-Based Ionic Liquids by Artificial Neural Network
inferred← citeshttps://doi.org/10.3390/molecules26092454openalex-snapshot
Group Contribution Estimation of Ionic Liquid Melting Points: Critical Evaluation and Refinement of Existing Models
inferred← citeshttps://doi.org/10.3390/molecules28207231openalex-snapshot
The System of Self-Consistent Models: The Case of Henry’s Law Constants
inferred← citeshttps://doi.org/10.5772/59271openalex-snapshot
Thermal Behaviour of Pure Ionic Liquids
inferred← citeshttps://doi.org/10.5936/csbj.201302004openalex-snapshot
COMPARISON AND ENUMERATION OF CHEMICAL GRAPHS
inferred← citeshttps://doi.org/10.61467/2007.1558.2023.v14i3.384openalex-snapshot
Prediction of the Melting Point of Ionic Liquids with Clustering and Noeuroevolution
inferred← citeshttps://doi.org/10.70675/50158fa9z2b5fz48cfzb16cz4f95b63cb87fopenalex-snapshot
Property-enriched fragment descriptors for adaptive QSAR
inferred← citeshttps://doi.org/10.9766/kimst.2013.16.5.690openalex-snapshot
Review : Ionic Liquids as Green Solvent
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.