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Found 4 papers, 2 papers with code
Extracting Material Property Measurement Data from Scientific Articles
Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets.
Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models
The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors.
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions.
Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications.
