741 citations · 1k across the 6 of their papers we have counts for
6 papers
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora
Monica Puerto, Mason Kellett, Rodanthi Nikopoulou +4
As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topi…
Deep learning for molecular design - a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge +1
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, musi…
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
Daniel C. Elton, Dhruv Turakhia, Nischal Reddy +4
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information a…
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Zois Boukouvalas, Daniel C. Elton, Peter W. Chung +1
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…
Machine Learning of Energetic Material Properties
Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas +4
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.…
Applying machine learning techniques to predict the properties of energetic materials
Daniel C. Elton, Zois Boukouvalas, Mark S. Butrico +2
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a sma…