Liionsden: Webapp for the Analysis of Li-ion Battery Materials
A Django webapp to parse and store battery cycler data and metadata
Posts by Collection
portfolio
Paricia: Hydroclimatic Data Management Platform
A Django webapp to manage hydroclimatic time-series data
SMACT: Semiconducting Materials by Analogy and Chemical Theory
Python package for cheap screening of inorganic compounds and compositions
SupraShare: Sharing trained machine learning models
Dash Apps for sharing interactive versions of trained machine learning models in the domain of chemistry
Surfaxe: Automated First-Principles Calcualtions of Surface Properties
A package to assist with reliable and systematic calcualtions of surface properties
Thermoplotter: Streamlined Analysis of Thermoelectric Properties
A simplified analysis and plotting workflow for thermoelectric properties
publications
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).
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).
Applications of Crystal Structure Prediction–Inorganic and Network Structures: General Discussion
Published in Faraday Discussions, 2018
General discussion that followed presentation of our work on oxidation states at an RSC Faraday Discussions meeting on structure prediction.
Recommended citation: V Burger et al. "Applications of crystal structure prediction–inorganic and network structures: general discussion." Faraday Discuss., 211 (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).
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).
Data-driven Discovery of Photoactive Quaternary Oxides using First-principles Machine Learning
Published in Chemistry of Materials, 2019
A computational sift through over 1 million hypothetical compounds to identify new quaternary oxides.
Recommended citation: D W Davies, K T Butler and A Walsh. "Data-driven Discovery of Photoactive Quaternary Oxides using First-principles Machine Learning" Chem. Mater., 31 (2019).
Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals
Published in Chemistry of Materials, 2019
We demonstrate a compact machine learning representation based on group theory for tilted and distorted octahedral crystal structures.
Recommended citation: K. Morita et al, "Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals" Chem. Mater., 34 (2022).
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).
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).
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).
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).
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).
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).
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).
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).
talks
Using chemical heuristics to filter the search space of possible compounds
Published:
A small workshop at SNU, with delegates also from Yonsei University and Ewha Women’s University.
High-throughput computation of all inorganic materials
Published:
My first talk at an international conference could not have been in a more beautiful location.
Low-cost high-throughput screening of all inorganic compounds
Published:
Presented work relating to our Chem paper on screening all inorganic materials.
Supercharging the materials discovery process with machine learning
Published:
I won the prize for best use of machine learning. You can read the write up on the University of Bath HPC pages here.
(Invited) Predicting optical band gaps in high throughput
Published:
Slides available here.
(Invited) Property prediction using first-principles machine learning
Published:
Invited talk at workshop between Materials Science departments of various universities in Seoul, hosted at Yonsei University.
(Invited) High-throughput discovery of quaternary oxide materials using first-principles machine learning
Published:
Departmental seminar, hosted by Dr Piero Canepa.
(Invited) Chemical heuristics, ML and first-principles calculations for high-throughput materials discovery
Published:
Departmental seminar given at A*STAR, Singapore.
Combining chemical heuristics, machine Learning, and first-principles calculations for rapid materials screening
Published:
Presented at the 14th Royal Society of Chemistry (RSC) Materials Chemistry conference.
Descriptors for the nature of electron and hole charge carriers from first-principles calculations data
Published:
Slides available here.
Computational search of anion redox Li-ion battery composition space
Published:
Slides available here.
Virtual high-throughput screening of photoactive quaternary oxides
Published:
Slides available here.
(Invited) Low cost descriptors for surrogate modelling of energy generation and storage
Published:
Slides available here.
(Invited) Combining chemical heuristics, automated calculations and machine learning in the search for solar active materials
Published:
UCL Department of Chemistry Seminar.
Descriptors for electron and hole charge carriers in metal oxides
Published:
Recorded presentation available here.
teaching
Computational Chemistry Labs
Undergraduate course, University of Bath, Chemistry, 2014
Lab demonstrating during my time as a PhD student involved helping first and second year undergraduates get to grips with compchem fundamental such as geometry relaxation and forcefields.
Chemistry and Maths Tutorials
Undergraduate course, University of Bath, Chemistry, 2015
Throughout my time as a PhD student, I enjoyed taking small group tutorials and problem classes to reinforce lecture content.
Energy Materials: Design, Discovery and Data
Postgraduate course, Yonsei University, Materials Science and Engineering, 2016
I spent the Easter period of 2016, 2017 and 2019 at Yonsei University, giving postraduate lectures and workshops on computational investigation into energy materials.
Data-driven Discovery for Photovoltaics
Postgraduate course, University of Bath, Physics and Chemistry, 2018
Towards the end of my time as a PhD student in Bath I helped to deliver workshops on the role of data and databases in materials discovery.
Conference Tutorials
Specialised tutorials, Various conferences, 2019
I have had the opportunity to give tutorial talks at various international conferences, including CECAM workshops and MRS Meetings.
Python for Final Year Chemists
Undergraduate course, University College London, Chemistry, 2020
At UCL I was involved in a new course which aimed to get final year chemistry students familiar with the rudiments of Python.
Essential Software Engineering for Researchers
Graduate course, Imperial College London Graduate School, 2021
As part of my role in the Research Software Engineering Team at Imperial College London, I deliver a course on software engineering best practices for researchers.
Using Git to Code, Collaborate and Share
Graduate course, Imperial College London Graduate School, 2021
As part of my role in the Research Software Engineering Team at Imperial College London, I deliver a course on using Git and Github to graduate students.