Research
My research program combines first-principles electronic-structure theory, semiempirical methods, molecular dynamics, and machine-learning force fields to connect atomic mechanisms with optoelectronic materials performance.
At York, I work on defect dynamics at grain boundaries in hybrid semiconductors and on developing machine-learning force fields for realistic, large-scale simulations.
I study the mechanisms that control phase stability, crystallization, solvent effects, and morphology in metal-halide perovskites using DFT and ab initio molecular dynamics.
A major direction is the development and validation of DFTB parameters for large periodic and non-periodic perovskite systems, including 3D, 2D, and heterostructured iodide perovskites.
I use atomistic modelling to understand layered perovskites, quantum dots, dopants, surfaces, interfaces, and device-relevant behavior for photovoltaics and light-emitting applications.
Density functional theory, DFT-1/2, semiempirical DFTB, and electronic-structure analysis for semiconductor materials.
Molecular dynamics and ab initio molecular dynamics to follow phase transitions, surface processes, ligand interactions, and solvent-assisted transformations.
Machine-learning force-field development to reach larger structural models and longer timescales while retaining a physically grounded link to quantum-mechanical data.