Cover Image

Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rittig, Jan G., Hicham, Karim Ben, Schweidtmann, Artur M., Dahmen, Manuel, Mitsos, Alexander
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)

Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)

Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)

Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
by: Schweidtmann, Artur M., et al.
Published: (2022)

GraphXForm: Graph transformer for computer-aided molecular design
by: Pirnay, Jonathan, et al.
Published: (2024)

Differentiable Thermodynamic Phase-Equilibria for Machine Learning
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Federated Learning from Molecules to Processes: A Perspective
by: Rittig, Jan G., et al.
Published: (2025)

Graph Machine Learning for Design of High-Octane Fuels
by: Rittig, Jan G., et al.
Published: (2022)

Solution-corrected Constant Potential Model for CO2 Electrocatalysis in Ionic Liquids
by: Sun, Jikai, et al.
Published: (2025)

Using the Haken-Strobl-Reineker Model to Determine the Temperature Dependence of the Diffusion Coefficient
by: Barford, William
Published: (2024)

A Systematic Evaluation of Molecular Mixture Behavior Prediction
by: Leenhouts, Roel J., et al.
Published: (2026)

A Size-Dependent Ideal Solution Model for Liquid-Solid Phase Equilibria Prediction in Aqueous Organic Solutions
by: Alliston, Spencer P., et al.
Published: (2024)

Hydrated Silicate Ionic Liquids: Ionic Liquids for Silicate Material Synthesis
by: Vandenabeele, Dries, et al.
Published: (2024)

Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors
by: Hayer, Nicolas, et al.
Published: (2024)

Optimization with Trained Machine Learning Models Embedded
by: Schweidtmann, Artur M., et al.
Published: (2022)

Enhanced Ionic Conductivity of confined Ionic-Liquid in Angstrom-scale 2D channels
by: Yang, Jing, et al.
Published: (2026)

Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
by: Wagner, Jens, et al.
Published: (2026)

Chronoamperometry with Room-Temperature Ionic Liquids: Sub-Second Inference Techniques
by: France, Kordel K.
Published: (2025)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
by: Mayfrank, Daniel, et al.
Published: (2023)

Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
by: Velioglu, Mehmet, et al.
Published: (2026)

Thermal Conductivity of Metastable Ionic Liquid [$C_{2}mim$][$CH_{3}SO_{3}$]
by: Lozano-Martín, Daniel, et al.
Published: (2024)

Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study
by: Gupta, Aryan
Published: (2025)

Open-Source Fermionic Neural Networks with Ionic Charge Initialization
by: Pranesh, Shai, et al.
Published: (2024)

Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
by: Pultar, Felix, et al.
Published: (2024)

Protic Ionic Liquids in Contrast to Aprotic Analogs: Transport Properties and Electrochemical Reactivity
by: Masayoshi Watanabe, et al.
Published: (2026)

Ionic Liquid Molecular Dynamics Simulation with Machine Learning Force Fields: DPMD and MACE
by: Park, Anseong, et al.
Published: (2025)

Energy‐Efficient Styrene Production by Incorporating Ionic Liquid‐Based Separation Technology
by: Yang Lei, et al.
Published: (2025)

A Simple Molecular Model for Hydrated Silicate Ionic Liquids, a Realistic Zeolite Precursor
by: Vekeman, Jelle, et al.
Published: (2024)

Recent Advances in Metal‐Based Catalysts for Nitrile Hydration to Amides: Mechanistic Aspects
by: Fatima‐Ezzahraa Essebbar, et al.
Published: (2025)

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes
by: Chen, Junmin, et al.
Published: (2025)

Experimentally Accurate Graph Neural Network Predictions of Core-Electron Binding Energies
by: Fouda, Adam E. A., et al.
Published: (2026)

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction
by: Uchiyama, Ryoichi, et al.
Published: (2026)

Recent Advances in the Utilization of Imidazolium‐Based Ionic Liquids for Chemical‐Enhanced Oil Recovery: A Comprehensive Review
by: Noran Mousa, et al.
Published: (2025)

Electric Field Induced Associations in the Double Layer of Salt-in-Ionic-Liquid Electrolytes
by: Markiewitz, Daniel M., et al.
Published: (2024)