arXiv subjects

Daniel C. Elton

Publications and source records attributed to Daniel C. Elton.

23 records · Page 2Linked to original sources

arXiv2018

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. Further, analysis is applied to individual molecules in order to explore the contribution of bonding motifs to these properties. Feature descriptors evaluated include Morgan fingerprints, E-state vectors, a custom "sum over bonds" descriptor, and coulomb matrices. Algorithms discussed include kernel ridge regression, least absolute shrinkage and selection operator ("LASSO") regression, Gaussian process regression, and the multi-layer perceptron (a neural network). Effects of regularization, kernel selection, network parameters, and dimensionality reduction are discussed. We determine that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials.

cond-mat.mtrl-sci↗

arXiv2018

Applying machine learning techniques to predict the properties of energetic materials

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 small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

cond-mat.mtrl-sci↗

arXiv2017

The origin of the Debye relaxation in liquid water and fitting the high frequency excess response

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 microscopic phenomena underlying both of these features. To better understand the molecular origin of Debye peak we ran large scale molecular dynamics simulations and performed several different distance-dependent decompositions of the low frequency dielectric spectra, finding that it involves processes that take place on scales of 1.5-2.0 nm. We also calculated the $k$-dependence of the Debye relaxation, finding it to be highly dispersive. These findings are inconsistent with models that relate Debye relaxation to local processes such as the rotation/translation of molecules after H-bond breaking. We introduce the spectrumfitter Python package for fitting dielectric spectra and analyze different ways of fitting the high frequency excess, such as including one or two additional Debye peaks. We propose using the generalized Lydanne-Sachs-Teller (gLST) equation as a way of testing the physicality of model dielectric functions. Our attempts at fitting the experimental spectrum using the gLST relation as a constraint indicate that the traditional way of fitting the excess response with secondary and tertiary Debye relaxations is problematic. All of our work is consistent with the recent theory of Popov et al. (2016) that Debye relaxation is due to the migration of Bjerrum-like defects in the hydrogen bond network. Under this theory, the mechanism of Debye relaxation in liquid water is similar to the mechanism in ice, but the heterogeneity and power-law dynamics of the H-bond network in water results in excess response on the high frequency side of the peak.

cond-mat.soft↗

arXiv2015

The hydrogen bond network of water supports propagating optical phonon-like modes

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 Raman and infrared spectroscopies. The assignment of spectral peaks depends on whether they are collective modes or single molecule motions. Vibrational modes in liquids are usually considered to be associated to the motions of single molecules or small clusters. Using molecular dynamics simulations we find dispersive optical phonon-like modes in the librational and OH stretching bands. We argue that on subpicosecond time scales these modes propagate through water's hydrogen bond network over distances of up to two nanometers. In the long wavelength limit these optical modes exhibit longitudinal-transverse splitting, indicating the presence of coherent long range dipole-dipole interactions, as in ice. Our results indicate the dynamics of liquid water have more similarities to ice than previously thought.

cond-mat.soft↗

arXiv2014

Polar nanoregions in water - a study of the dielectric properties of TIP4P/2005, TIP4P/2005f and TTM3F

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 function along with one dimensional and two dimensional dipole correlation functions. We find that the introduction of flexibility alone does not significantly affect dipole correlation and only affects $\varepsilon(ω)$ at high frequencies. By contrast the introduction of polarizability increases dipole correlation and yields a more accurate $\varepsilon(ω)$. Additionally the introduction of polarizability creates temperature dependence in the dipole moment even at fixed density, yielding a more accurate value for $d \varepsilon / d T$ compared to non-polarizable models. To better understand the physical origin of the dielectric properties of water we make analogies to the physics of polar nanoregions in relaxor ferroelectric materials. We show that $\varepsilon(ω,T)$ and $τ_D(T)$ for water have striking similarities with relaxor ferroelectrics, a class of materials characterized by large frequency dispersion in $\varepsilon(ω,T)$, Vogel-Fulcher-Tamann behaviour in $τ_D(T)$, and the existence of polar nanoregions.

cond-mat.soft↗