My publications are curated below in six categories: materials & chemicals, alloy design, pharmaceuticals & healthcare, solid state quantum physics, ultracold atomic gases, and visual perception. Publication metrics are available at Google Scholar. Six of these publications compose my PhD thesis. A pdf copy of any publication may be downloaded by clicking on the image of the paper.
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange Accepted for publication in Digital Discovery & arXiv:2402.00572 | |
Artificial Intelligence (AI) Futures: India-UK Collaborations Emerging from the 4th Royal Society Yusuf Hamied Workshop | |
A game theory-inspired algorithm for automating the design of non-periodic integral 3D woven composite preforms without scale limitations using a manufacturing-based parameterization | |
Probabilistic selection and design of concrete using machine learning | |
Unveil the unseen: exploit information hidden in noise | |
Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning | |
OPTIMADE: an API for exchanging materials data Nature Scientific Data 8, 217 (2021) and Research Highlight in Nature Review Materials (2021) | |
OPTIMADE API specification | |
Enhancing NEMD with improved sampling of shear rates to model viscosity and correction of systematic errors in modelling density: Application to linear and light branched alkanes | |
Predicting the State of Charge and Health of Batteries using Data-Driven Machine Learning | |
Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data | |
Predicting physical properties of alkanes with neural networks | |
Structure–Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning Matter 1, 219 (2019) and accompanying commentary Matter 1, 26 (2019) | |
Materials data validation and imputation with an artificial neural network | |
Method and system for designing a material Patents GB1302743, EP14153898, US2014/177578 (2013) |
Machine learning superalloy microchemistry and creep strength from physical descriptors | |
Design of a Ni-based Superalloy for Laser Repair Applications using Probabilistic Neural Network Identification | |
Design of Materials with Alchemite™ | |
Accelerating the Design of Automotive Catalyst Products Using Machine Learning | |
Machine learning predictions of superalloy microstructure | |
Au-Ge alloys for wide-range low-temperature on-chip thermometry | |
Probabilistic neural network identification of an alloy for direct laser deposition | |
Probabilistic design of a molybdenum-base alloy using a neural network | |
Design of a nickel-base superalloy using a neural network | |
Alloy composition Patents GB1307535, EP2796581, US20140322068 (2014) | |
Nickel Alloy Composition Patents GB201408536, EP2944704B1, US2015/0329941 (2014) | |
Alloy composition Patents GB1307533, EP2796580B1, US2016/0369379 (2014) | |
A nickel alloy Patents GB1309404, EP2805784B1, US2014/0348689 (2014) | |
Alloys based on Cr-Cr2Ta containing Si | |
Grain growth behaviour during near-γ' solvus thermal exposures in a polycrystalline nickel-base superalloy | |
Molybdenum Alloy Composition Patents GB201307535D0, EP2796581B1, US9347118B2 (2013) |
Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modelling | |
Prediction of in vivo pharmacokinetic parameters and time-exposure curves in rats using machine learning from chemical structure | |
Imputation of Sensory Properties Using Deep Learning | |
An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials | |
Deep Imputation on Large-Scale Drug Discovery Data | |
Data Imputation Through Deep Learning Innovations in Pharmaceutical Technology Autumn/Winter, 42 (2020) | |
Machine learning to predict mesenchymal stem cell efficacy for cartilage repair | |
Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data Journal of Chemical Information and Modeling 60, 2848 (2020) | |
Imputation versus prediction: applications in machine learning for drug discovery | |
Imputation of Assay Bioactivity Data using Deep Learning Journal of Chemical Information and Modeling, 59, 1197 (2019) |
Absence of diagonal force constants in cubic Coulomb crystals Proceedings of the Royal Society A 476, 20200518 (2020) and Supplemental Material | |
A tail-regression estimator for heavy-tailed distributions of known tail indices and its application to continuum quantum Monte Carlo data | |
Long-lived non-equilibrium superconductivity in a non-centrosymmetric Rashba semiconductor | |
Direct evaluation of the force constant matrix in quantum Monte Carlo | |
Band structure interpolation using optimized local orbitals from linear-scaling density functional theory | |
Multi-particle instability in a spin-imbalanced Fermi gas | |
Quantum Order-by-Disorder in Strongly Correlated Metals | |
Jastrow correlation factor for periodic systems | |
Pseudopotential for the electron-electron interaction | |
Extracting semiconductor band gap zero-point corrections from experimental data | |
High-fidelity contact pseudopotentials and p-wave superconductivity | |
Quantum Monte Carlo study of the two-dimensional ferromagnet | |
Fluctuation-induced pair density wave in itinerant ferromagnets | |
Itinerant ferromagnetism with finite ranged interactions | |
Field-Tuned Quantum Phase Transition in the Insulating Regime of Ultrathin Amorphous Bi Films | |
Microscopic theory of the magnetoresistance of disordered superconducting films | |
Resistance jumps and the nature of the finite-flux normal phase in ultra-thin superconducting cylinders | |
Strategies for improving the efficiency of quantum Monte Carlo calculations | |
First principles calculation of conductance and current flow through low-dimensional superconductors | |
Theory of quantum paraelectrics and the metaelectric transition Editors' Suggestion in Phys. Rev. B 81, 024102 (2010) | |
Inhomogeneous phase formation on the border of itinerant ferromagnetism Editors' Suggestion in Phys. Rev. Lett. 103, 207201 (2009) and Spotlight Viewpoint Commentary Physics 2, 93 (2009) | |
Diffusion Monte Carlo study of a valley degenerate electron gas and application to quantum dots Phys. Rev. B 78, 195310 (2008) and Virtual Journal of Nanoscale Science & Technology 18, 21 (2008) | |
Many-flavor electron gas approach to electron-hole drops |
Temporal fluctuation induced order in conventional superconductors | |
Diffusion Monte Carlo study of a spin-imbalanced two-dimensional Fermi gas with attractive interactions | |
Communal pairing in spin-imbalanced Fermi gases | |
Effective-range dependence of two-dimensional Fermi gases | |
Effective range dependence of resonant Fermi gases | |
Pseudopotential for the 2D contact interaction | |
Pseudopotentials for an ultracold dipolar gas | |
High-fidelity pseudopotentials for the contact interaction Phys. Rev. A 90, 033626 (2014) and Python program for pseudopotential generation | |
Inhomogeneous state of few-fermion superfluids | |
Exploring exchange mechanisms with a cold atom gas | |
Ferromagnetic spin correlations in a few-fermion system | |
Line of Dirac monopoles embedded in a Bose-Einstein condensate Phys. Rev. A 86, 021605(R) (2012) and Kaleidoscope in August 2012 | |
Itinerant ferromagnetism in an interacting Fermi gas with mass imbalance | |
Effect of three-body loss on itinerant ferromagnetism in an atomic Fermi gas | |
Itinerant ferromagnetism in a two-dimensional atomic gas | |
Dynamical instability of a spin spiral in an interacting Fermi gas as a probe of the Stoner transition | |
A repulsive atomic gas in a harmonic trap on the border of itinerant ferromagnetism Phys. Rev. Lett. 103, 200403 (2009) and Virtual Journal of Atomic Quantum Fluids 1 (2009) | |
Itinerant ferromagnetism in an atomic Fermi gas: Influence of population imbalance | |
Superfluidity at the BEC-BCS crossover in two-dimensional Fermi gases with population and mass imbalance |
Visibility prediction software: five factors of contrast perception for the vision impaired in the real world Designing Inclusive Systems, Springer, London, pp. 93-102 (2012). ISBN: 978-1-4471-2866-3 | |
A Colour Contrast Assessment System: Design for People with Visual Impairment Designing Inclusive Interactions, Springer Verlag, pp. 101-112 (2010). ISBN: 978-1-84996-165-3 | |
British Standard BS 8493:2008+A1:2010 Light reflectance value (LRV) of a surface. Method of test | |
The Contrast Guide: Design and Contrast Specifications for Environments and Products | |
Measurement for a more visible world: colour contrast and visual impairment Measurement, sensation and cognition, pp. 134-138 (2009). ISBN: 978-0-946754-56-4 |