I'm a PhD candidate in the Risko Lab, Department of Chemistry & Center for Applied Energy Research, University of Kentucky. I use first-principles methods and machine learning to study energy materials: battery electrodes, catalysts, and hybrid perovskites.
- 🔬 Battery electrodes: amorphous molybdenum sulfide chalcogels
- ⚛️ Catalysis: Ni–Cu bimetallic catalysts for renewable diesel production
- ☀️ Perovskites: 2D/3D hybrid perovskite heterostructures for solar cells
- 🧱 Metal-organic frameworks: hydrogen addition and water adsorption
- 🤖 Growing focus on ML-accelerated materials discovery (Bayesian optimization, MACE, FAIRChem)
- 🎓 Google Scholar · ORCID · Risko Lab
| Project | Description |
|---|---|
| Ni–Cu Bayesian Optimization | BoTorch/GPyTorch workflow screening ethylidyne adsorption sites on NiCu alloy surfaces using ASE and MACE potentials |
| Chalcogel DFT/AIMD Workflow | DFT/AIMD workflow for amorphous MoₓSᵧ chalcogel structural and electronic characterization, including XANES core-hole excitation energy alignment |
| Perovskite Structural Analysis | Python pipeline extracting Pb–I bond lengths and Pb–I–Pb/I–Pb–I angles from VASP CONTCAR files for 2D Ruddlesden-Popper perovskites |
| MOF DFT Analysis | DFT-based analysis of hydrogen addition and Bader charge redistribution on water adsorption in metal-organic frameworks using VASP |
| Adsorbate Site Screening | Symmetry-based site enumeration and adsorbate placement on relaxed metal slabs, with post-DFT dissociation/desorption screening |
| VASP Structure Library | Curated VASP POSCAR structures (surface slabs, doped oxides, published geometries) with documented sources, DOI references, and ASE scripts for building new structures |
| Data Science Coursework | Six worked data science examples in Python |
Full list on Google Scholar