HetSys CDT Thesis Library
2024-25: Cohort 2
Connor Allen Stewart: From first principles to phase diagrams : modelling Ti and its alloys at extreme conditions.
Iain Best: Uncertainty quantification with machine learning interatomic potentials using conformal prediction
Adam Fisher: Modelling the extraordinary strength of superalloys
Joe Gilkes: Chemical reaction discovery with Kinetica.JL : exploration and simulation of chemical degradation
Peter Lewin-Jones: Liquid drop impacts: when trapped gas nanofilms lead to bouncing
Tadashi Matsumoto: Statistical calibration of probabilistic numerical methods for parabolic PDE-constrained inverse problems
Charlotte Rogerson: Investigation of the impact of equation of state models on inertial confinement fusion simulations
Lakshmi Shenoy: Modelling defects in iron and its alloys with machine-learned interatomic potentials
Alisdair Soppitt: The statistics of reactive mixing in rough channels
James M. Targett:The effect of functionalisation on thermoelectric properties of molecular junctions
2023-24: Cohort 1
Andrew Angus: Probabilistic modelling of stimulated raman back-scatter in laser direct-drive fusion plasmas at ignition scale
Katarina Blow: The role of kinetics, order parameters, and entropy in computing crystallisation rates
Matthew Harrison: Uncertainty quantification and propagation of hydraulic conductivity fields across scales and their effect on flow and solute transport
Idil Ismail: Accelerating chemical reaction discovery using graph-driven searches and machine learning strategies.
Jingbang Liu: Fluctuating hydrodynamics of nanoscale thin films.
Carlo Maino: Machine learned potentials for theoretical spectroscopy of molecules in solvent
Aravinthen Rajkumar: Structure-property relations for block copolymers: a molecular data-driven approach
Christopher Woodgate: Atomic arrangements in multicomponent alloys : first-principles theory, atomistic modelling, and implications for magnetic properties