Computational materials scientist — first-principles and machine-learning modelling of energy-storage materials.
I work at the intersection of density-functional theory, ab initio and machine-learned-force-field molecular dynamics, and data-driven materials screening. Most of my code exists to turn raw simulation output into physical observables, reproducibly.
- Solid-state electrolytes — halide and oxide Li⁺/Na⁺ conductors, glass and melt-quench models, ionic transport
- Li–S battery chemistry — polysulfide adsorption, catalytic conversion, anchoring materials
- Thermal transport — lattice thermal conductivity from Green–Kubo and Boltzmann transport, phonon anharmonicity
- ML for materials — structure datasets, descriptors (SOAP), equivariant graph representations, property regression
VASP · Quantum ESPRESSO · phono3py / phonopy · LAMMPS · MLFF-MD
· Wannier90 · pymatgen · matminer · ASE · NumPy/SciPy · PyTorch
| repo | what it does |
|---|---|
| magnetic-symmetry-analyzer | magnetic space group, spin group and symmetry operations from a CIF/POSCAR — with automatic altermagnet / AFM / FM classification (spglib + spinspg) |
| green-kubo-kappa | Green–Kubo lattice thermal conductivity from a VASP MLFF ML_HEAT heat-flux trajectory — unbiased HFACF, running integral, plateau analysis |
| battery-materials-ml | end-to-end pipeline: Materials Project + Alexandria → de-duplicate → sanitise → SOAP / graph features → baseline property model with grouped CV |
Handle: WalterWhite1611. Reach me through the profile email.