• Publications
  • Influence
Deep learning for molecular generation and optimization - a review of the state of the art
TLDR
A recent groundswell of work which uses deep learning techniques to generate and optimize molecules and how these techniques improve the quality of existing molecules is reviewed.
Deep learning for molecular design—a review of the state of the art
TLDR
Several important high level themes emerge, 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.
Applying machine learning techniques to predict the properties of energetic materials
TLDR
This work presents a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints - and concludes that the best featurizing was sum over bond counting, and the best model was kernel ridge regression.
Machine Learning of Energetic Material Properties
TLDR
It is determined that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials.
Exclusion Zone Phenomena in Water—A Critical Review of Experimental Findings and Theories
TLDR
It is argued that Schurr’s theory based on diffusiophoresis presents a compelling alternative explanation for the core EZ phenomenon and makes predictions about the growth of the EZ with time which have been confirmed by Florea et al. and others.
Accurate estimation of third-order moments from turbulence measurements
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
The hydrogen-bond network of water supports propagating optical phonon-like modes
TLDR
The dynamics of liquid water have more similarities to ice than previously thought and it is argued that on subpicosecond time scales these modes propagate through water's hydrogen-bond network over distances of up to 2 nm.
Phonon Lifetimes and Thermal Conductivity of the Molecular Crystal α-RDX
The heat transfer properties of the organic molecular crystal α-RDX were studied using three phonon scattering based thermal conductivity models. It was found that the widely used Peierls-Boltzmann
The origin of the Debye relaxation in liquid water and fitting the high frequency excess response.
  • D. Elton
  • Physics
    Physical chemistry chemical physics : PCCP
  • 5 April 2017
TLDR
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.
Self-explaining AI as an Alternative to Interpretable AI
  • D. Elton
  • Computer Science
    AGI
  • 12 February 2020
TLDR
It is argued it is important that deep learning based systems include a "warning light" based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution.
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