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. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules-in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

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 ≈300 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.

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, here 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 2 nm. 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.

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.

Key features differentiate recovery colleges from traditional services, including an empowering environment, enabling relationships, and growth orientation. Service users who lack confidence, those with whom services struggle to engage, those who will benefit from exposure to peer role models, and those lacking social capital may benefit most. As the first testable characterization of mechanisms and outcomes, the change model allows formal evaluation of recovery colleges.

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 ɛ(ω) at high frequencies. By contrast the introduction of polarizability increases dipole correlation and yields a more accurate ɛ(ω). Additionally, the introduction of polarizability creates temperature dependence in the dipole moment even at fixed density, yielding a more accurate value for dɛ/dT 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 ɛ(ω, T) and τD(T) for water have striking similarities with relaxor ferroelectrics, a class of materials characterized by large frequency dispersion in ɛ(ω, T), Vogel-Fulcher-Tammann behaviour in τD(T), and the existence of polar nanoregions.

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 means of third-order moments. In hydrodynamics, accurate measurement of thirdorder moments requires large amounts of data because the probability distributions of velocity-differences are nearly symmetric and the third-order moments are relatively small. Measurements of the energy cascade rate in solar wind turbulence have recently been performed for the first time, but without careful consideration of the accuracy or statistical uncertainty of the required third-order moments. This paper investigates the statistical convergence of third-order moments as a function of the sample size N. It is shown that the accuracy of the third-moment (δv ) 3 depends on the number of correlation lengths spanned by the data set and a method of estimating the statistical uncertainty of the thirdmoment is developed. The technique is illustrated using both wind tunnel data and solar wind data.

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