Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Brozos, Christoforos, Rittig, Jan G., Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
by: Rittig, Jan G., et al.
Published: (2022)
by: Rittig, Jan G., et al.
Published: (2022)
Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)
by: Rittig, Jan G., et al.
Published: (2024)
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)
by: Pavšek, Jan, et al.
Published: (2026)
Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)
by: Rittig, Jan G., et al.
Published: (2025)
Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)
by: Hicham, Karim K. Ben, et al.
Published: (2026)
DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)
by: Pavšek, Jan, et al.
Published: (2025)
GraphXForm: Graph transformer for computer-aided molecular design
by: Pirnay, Jonathan, et al.
Published: (2024)
by: Pirnay, Jonathan, et al.
Published: (2024)
Federated Learning from Molecules to Processes: A Perspective
by: Rittig, Jan G., et al.
Published: (2025)
by: Rittig, Jan G., et al.
Published: (2025)
Predicting tactile sensory attributes of personal care emulsions based on instrumental characterizations: A review
by: Elie Akanny, et al.
Published: (2024)
by: Elie Akanny, et al.
Published: (2024)
A Systematic Evaluation of Molecular Mixture Behavior Prediction
by: Leenhouts, Roel J., et al.
Published: (2026)
by: Leenhouts, Roel J., et al.
Published: (2026)
Uncovering sustainable personal care ingredient combinations using scientific modelling
by: Bhattacharya, Sandip, et al.
Published: (2026)
by: Bhattacharya, Sandip, et al.
Published: (2026)
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
by: Schweidtmann, Artur M., et al.
Published: (2022)
by: Schweidtmann, Artur M., et al.
Published: (2022)
Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)
by: Rittig, Jan G., et al.
Published: (2022)
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
by: Panapitiya, Gihan, et al.
Published: (2024)
by: Panapitiya, Gihan, et al.
Published: (2024)
Differentiable Thermodynamic Phase-Equilibria for Machine Learning
by: Hicham, Karim K. Ben, et al.
Published: (2026)
by: Hicham, Karim K. Ben, et al.
Published: (2026)
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
by: Shinkle, Emily, et al.
Published: (2024)
by: Shinkle, Emily, et al.
Published: (2024)
Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction
by: Medina, Edgar Ivan Sanchez, et al.
Published: (2025)
by: Medina, Edgar Ivan Sanchez, et al.
Published: (2025)
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
by: Jiang, Shengli, et al.
Published: (2023)
by: Jiang, Shengli, et al.
Published: (2023)
Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
by: Marks, Jonah, et al.
Published: (2025)
by: Marks, Jonah, et al.
Published: (2025)
Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study
by: Gupta, Aryan
Published: (2025)
by: Gupta, Aryan
Published: (2025)
Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer
by: Pengmei, Zihan, et al.
Published: (2024)
by: Pengmei, Zihan, et al.
Published: (2024)
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
by: Zhu, Jianshen, et al.
Published: (2025)
by: Zhu, Jianshen, et al.
Published: (2025)
XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
by: Grizzi, Vitor F., et al.
Published: (2026)
by: Grizzi, Vitor F., et al.
Published: (2026)
Toward Routine CSP of Pharmaceuticals: A Fully Automated Protocol Using Neural Network Potentials
by: Glick, Zachary L., et al.
Published: (2025)
by: Glick, Zachary L., et al.
Published: (2025)
Highly Accurate Real-space Electron Densities with Neural Networks
by: Cheng, Lixue, et al.
Published: (2024)
by: Cheng, Lixue, et al.
Published: (2024)
GemNet: Universal Directional Graph Neural Networks for Molecules
by: Gasteiger, Johannes, et al.
Published: (2021)
by: Gasteiger, Johannes, et al.
Published: (2021)
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)
by: Peng, Jiayu, et al.
Published: (2024)
Implicit Delta Learning of High Fidelity Neural Network Potentials
by: Thaler, Stephan, et al.
Published: (2024)
by: Thaler, Stephan, et al.
Published: (2024)
Open-Source Fermionic Neural Networks with Ionic Charge Initialization
by: Pranesh, Shai, et al.
Published: (2024)
by: Pranesh, Shai, et al.
Published: (2024)
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)
by: Solé, Àlex, et al.
Published: (2025)
$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
by: Khrabrov, Kuzma, et al.
Published: (2024)
by: Khrabrov, Kuzma, et al.
Published: (2024)
StringNET: Neural Network based Variational Method for Transition Pathways
by: Han, Jiayue, et al.
Published: (2024)
by: Han, Jiayue, et al.
Published: (2024)
Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
by: Zhang, Xuan, et al.
Published: (2026)
by: Zhang, Xuan, et al.
Published: (2026)
Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
by: Xia, Junfan, et al.
Published: (2025)
by: Xia, Junfan, et al.
Published: (2025)
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
by: Käser, Silvan, et al.
Published: (2024)
by: Käser, Silvan, et al.
Published: (2024)
ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures
by: Fan, Jinming, et al.
Published: (2026)
by: Fan, Jinming, et al.
Published: (2026)
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
by: Arjona-Medina, Jose, et al.
Published: (2024)
by: Arjona-Medina, Jose, et al.
Published: (2024)
Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
by: Koenig, Benjamin C., et al.
Published: (2025)
by: Koenig, Benjamin C., et al.
Published: (2025)
Similar Items
-
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024) -
Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024) -
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
by: Rittig, Jan G., et al.
Published: (2022) -
Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024) -
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)