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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…
Stretched Exponential Relaxation
Daniel C. Elton
This short report details the mathematical properties of the stretched exponential function and some of its applications in materials science. G(tau) distributions for different va…
A Phonon Boltzmann Study of Microscale Thermal Transport in -RDX Cook-Off
Francis G. VanGessel, Gaurav Kumar, Daniel C. Elton +1
The microscale thermal transport properties of RDX are believed to be major factors in the initiation process. In this study we present a thorough examination of phonon properti…
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.…
Using a monomer potential energy surface to perform approximate path integral molecular dynamics simulation of ab-initio water with near-zero added cost
Daniel C. Elton, Michelle Fritz, M. -V. Fernández-Serra
It is now established that nuclear quantum motion plays an important role in determining water's hydrogen bonding, structure, and dynamics. Such effects are important to include in…
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…