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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.… Expand
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Applying machine learning techniques to predict the properties of energetic materials
- D. Elton, Zois Boukouvalas, Mark S Butrico, M. Fuge, P. W. Chung
- Materials Science, Computer Science
- Scientific Reports
- 15 January 2018
TLDR
The origin of the Debye relaxation in liquid water and fitting the high frequency excess response.
- D. Elton
- Materials Science, Chemistry
- Physical chemistry chemical physics : PCCP
- 5 April 2017
We critically review the literature on the Debye absorption peak of liquid water and the excess response found on the high frequency side of the Debye peak. We find a lack of agreement on the… Expand
Accurate estimation of third-order moments from turbulence measurements
- J. Podesta, M. Forman, C. Smith, D. Elton, Y. Malécot, Y. Gagné
- Physics
- 22 January 2009
Abstract. Politano and Pouquet's law, a generalization of Kolmogorov's four-fifths law to incompressible MHD, makes it possible to measure the energy cascade rate in incompressible MHD turbulence by… Expand
Machine Learning of Energetic Material Properties
- Brian C. Barnes, D. Elton, +4 authors P. W. Chung
- Physics
- 17 July 2018
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysis… Expand
Deep learning for molecular generation and optimization - a review of the state of the art
- D. Elton, Zois Boukouvalas, M. Fuge, P. W. Chung
- Computer Science, Physics
- ArXiv
- 11 March 2019
TLDR
Polar nanoregions in water: a study of the dielectric properties of TIP4P/2005, TIP4P/2005f and TTM3F.
- D. Elton, M. Fernández-Serra
- Physics, Medicine
- The Journal of chemical physics
- 20 January 2014
We present a critical comparison of the dielectric properties of three models of water-TIP4P/2005, TIP4P/2005f, and TTM3F. Dipole spatial correlation is measured using the distance dependent Kirkwood… Expand
The hydrogen-bond network of water supports propagating optical phonon-like modes
- D. Elton, M. Fernandez-Serra
- Materials Science, Medicine
- Nature communications
- 23 July 2015
The local structure of liquid water as a function of temperature is a source of intense research. This structure is intimately linked to the dynamics of water molecules, which can be measured using… Expand
Understanding the dielectric properties of water
- D. Elton
- Chemistry
- 2016
of the Dissertation Understanding the dielectric properties of water
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- PDF
Self-explaining AI as an Alternative to Interpretable AI
- D. Elton
- Computer Science, Mathematics
- AGI
- 12 February 2020
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