Dr Daniel W Davies

Dr Daniel W Davies

Senior Research Software Engineer at Imperial College London

Publications

Investigation of factors affecting the stability of compounds formed by isovalent substitution in layered oxychalcogenides, leading to identification of Ba3Sc2O5Cu2Se2, Ba3Y2O5Cu2S2, Ba3Sc2O5Ag2Se2 and Ba3In2O5Ag2Se2

Published in Journal of Materials Chemistry C, 2022

Working with experimental researchers from Southampton Unversity, we investigate the relationship between stability and the thermodynamic and vibrational properties of several Ba-containing layered materials.

Recommended citation: G. J. Limburn et al., "Investigation of factors affecting the stability of compounds formed by isovalent substitution in layered oxychalcogenides, leading to identification of Ba3Sc2O5Cu2Se2, Ba3Y2O5Cu2S2, Ba3Sc2O5Ag2Se2 and Ba3In2O5Ag2Se2" J. Mater. Chem. C, 10 (2022).

Low Electronic Conductivity of Li7La3Zr2O12 (LLZO) Solid Electrolytes from First Principles

Published in Physical Review Materials, 2021

We demonstrate a first-principles scheme for modeling the electronic conductivity of nominally insulating materials, such as solid electrolytes for lithium-metal batteries.

Recommended citation: A. G. Squires et al., "Low Electronic Conductivity of Li7La3Zr2O12 (LLZO) Solid Electrolytes from First Principles" Phys. Rev. Mater., 6 (2021).

Surfaxe: Systematic Surface Calculations

Published in Journal of Open Source Software, 2021

An open-source code for preparing and analysing first-principles surface calculations.

Recommended citation: K. Brlec, D. W. Davies and D. O. Scanlon, "Surfaxe: Systematic Surface Calculations" JOSS, 6 (2021).

Bandgap lowering in mixed alloys of Cs2Ag(SbxBi1−x)Br6 double perovskite thin films

Published in Journal of Materials Chemistry A, 2020

Working with experimental researchers at Imperial College London, we investigate the dependence of band gap on the value of x in Cs2Ag(SbxBi1−x)Br6.

Recommended citation: Z Li et al., "Bandgap lowering in mixed alloys of Cs2Ag(SbxBi1−x)Br6 double perovskite thin films" J. Mater. Chem. A, 8 (2020).

Low-cost descriptors of electrostatic and electronic contributions to anion redox activity in batteries

Published in IOP SciNotes, 2020

In this study, we present practical procedures for using simple descriptors relating to the electrostatics (Madelung energy) and electronic structure (density of states) to help predict the redox activity of anions in inorganic compounds.

Recommended citation: D W Davies, B J Morgan, D O Scanlon and A Walsh, "Low-cost descriptors of electrostatic and electronic contributions to anion redox activity in batteries" IOP SciNotes, 1 (2020).

Modelling the Dielectric Constants of Crystals Using Machine Learning

Published in Journal of Physical Chemistry C, 2019

We apply two machine learning models (support vector regression and deep neural networks) to the challenge of predicting dielectric properties of inorganic materials.

Recommended citation: K Morita, D W Davies, K T Butler and A Walsh, "Modelling the Dielectric Constants of Crystals Using Machine Learning" J. Chem. Phys., 153 (2020).

Identification of Lone-Pair Surface States on Indium Oxide

Published in Journal of Physical Chemistry C, 2019

A joint experimental – computational study in which In 5s lone pairs are identified for the first time on the surface of indium oxide.

Recommended citation: D W Davies et al., "Identification of Lone-Pair Surface States on Indium Oxide" J. Phys. Chem. C, 123 (2019).

SMACT: Semiconducting Materials by Analogy and Chemical Theory

Published in Journal of Open Source Software, 2019

An open-source code for generating and evaluating large inorganic composition spaces.

Recommended citation: D W Davies et al., "SMACT: Semiconducting Materials by Analogy and Chemical Theory" JOSS, 4 (2019).

Machine Learning for Molecular and Materials Science

Published in Nature, 2018

Our perspective summarising exciting progress in machine learning for the chemical sciences.

Recommended citation: K T Butler, D W Davies, H Cartwright, O Isayev and A Walsh. "Machine Learning for Molecular and Materials Science" Nature, 559 (2018).

Materials Discovery by Chemical Analogy: Role of Oxidation States in Structure Prediction

Published in Faraday Discussions, 2018

In this study, we investigate the likelihood that transition metals adopt specific oxidation states based on the anions present in the material. We then use this information predictively to suggest a range of novel ternary metal halide compounds.

Recommended citation: D W Davies et al. "Materials Discovery by Chemical Analogy: Role of Oxidation States in Structure Prediction." Faradau Discuss., 211 (2018).

Computer-aided Design of Metal Chalcohalide Semiconductors: From Chemical Composition to Crystal Structure

Published in Chemical Science, 2018

In this study we use compositional screening, along with techniques based on structural analogy and global searches (evolutionary algorithms) to identify new three-component semiconductors.

Recommended citation: D W Davies et al. "Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure." Chem. Sci., 9 (2018).

Computational Screening of All Stoichiometric Inorganic Materials

Published in Chem, 2016

This is the first paper using SMACT. We enumerated all 2- 3- and 4-component ingorganic compositions up to a stoichiometric limit of 8.

Recommended citation: D W Davies et al. "Computational Screening of All Stochiometric Inorganic Materials." Chem, 1 (2016).