Neural network exchange-correlation functionals, differentiable SCF solvers, and equivariant architectures to accelerate quantum chemistry from first principles.
I am a PhD researcher at the Technical University of Munich working at the intersection of machine learning and quantum chemistry. My goal is to make density functional theory faster and more accurate by learning its hardest component, the exchange-correlation functional, directly from data.
I develop equivariant graph neural networks that operate on molecular electronic structure, differentiable self-consistent field solvers in JAX, and training paradigms that leverage physical derivatives on the Grassmannian manifold of density matrices.
Count words, characters, and tokens while compacting markdown-heavy text for tighter prompts and cleaner sharing.
Inspect molecular structures in an interactive viewer with alternate representations for geometry, graphs, and related views.
Convert between common quantum chemistry units quickly without leaving the browser or rebuilding your workflow.
Design ML architecture flowcharts interactively with color-coded blocks, snap alignment, and clean PDF figure export for papers.