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Found 7 papers, 2 papers with code
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
We test the predictive quality of the model for following scenarios: i) when CMC data for surfactants are present in the training of the model in at least one different temperature, and ii) CMC data for surfactants are not present in the training, i. e., generalizing to unseen surfactants.
Graph Neural Networks for Surfactant Multi-Property Prediction
A key factor in the predictive ability of QSPR and GNN models is the data available for training.
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations.
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent.
Graph neural networks for the prediction of molecular structure-property relationships
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design.
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.
Graph Machine Learning for Design of High-Octane Fuels
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
