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Found 5 papers, 3 papers with code
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora
With our accuracy results, we also introduce local interpretability model-agnostic explanations (LIME) of each prediction to provide a localized understanding of each prediction and to validate classifier decisions with our team of energetics experts.
Deep learning for molecular design - a review of the state of the art
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
Machine Learning of Energetic Material Properties
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.
Materials Science Chemical Physics Computational Physics
