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 | |
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) |
Modelling nicotine pharmacokinetic profile for e-cigarette using real time monitoring of consumer’s physiological measurements and mouth level exposure | |
Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modelling Journal of Chemical Information and Modeling, 64, 2624 (2024) | |
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 |