svatunek // lab

How does a reaction actually proceed? These projects use quantum chemical calculations to elucidate real organic reaction mechanisms: locating transition states and intermediates, mapping competing pathways, and explaining experimental observations from first principles.

You work closely with experimental data from collaborators and use DFT to answer questions like which step is rate-limiting, why one product forms over another, or what role the solvent plays. No prior programming experience required.

Can we predict how fast a reaction will be, explain why it goes that fast, or ideally both? These projects focus on bioorthogonal click reactions: correlating computed properties with experimental rate constants to build quantitative reactivity models.

You dig into the underlying electronic structure to uncover what actually controls reactivity and selectivity: whether it is distortion, orbital interactions, steric effects, or a combination. A great fit if you want to connect physical chemistry to real experimental data.

Machine learning is reshaping what is possible in computational chemistry. One major direction is ML interatomic potentials (MLIPs): models trained on QM data that can run molecular dynamics at a fraction of the cost, opening up systems and timescales that were previously out of reach.

A second direction is purely data-driven: correlating computed or experimental descriptors with reactivity, selectivity, or properties to extract chemical insight and build predictive models. Ideal if you have Python experience and want to work at the intersection of ML and chemistry.

In collaboration with the TU Wien Mixed Reality Lab, we are developing immersive 3D visualisations of molecules and reactions for teaching and outreach. Imagine walking through a reaction mechanism at the atomic scale.

These projects involve creating and animating molecular 3D content using tools like Blender, Unity, or web-based viewers, and thinking about how to communicate chemistry effectively in an immersive environment. Great for students who enjoy the creative side.

Large language models are surprisingly capable research tools, but using them well requires careful engineering. We currently have two concrete projects in this area.

Agentic workflows: build an AI agent that autonomously searches the literature, extracts rate constants and conditions, and assembles a structured dataset. Tasks that currently take researchers hours of manual work.

Lab notebook digitisation: develop a pipeline that reads handwritten or PDF lab notebooks and converts them into machine-readable reaction records (JSON/structured data), ready for analysis or database ingestion.

Many chemistry problems benefit from better software: smarter automation, new analysis tools, or cleaner interfaces between existing programs. If you enjoy writing code, there is no shortage of interesting problems here.

Current opportunities include developing Python tools for reaction analysis, building web-based molecular viewers, automating quantum chemistry workflows, or contributing to open-source chemistry packages. The exact project can be shaped around your interests.

3D printing makes chemistry tangible. We print physical models for teaching and outreach: molecules, potential energy surfaces, transition state geometries, and other objects that are hard to grasp from a 2D drawing or a screen.

Projects involve converting quantum-chemical calculations into printable 3D models, working out how to represent abstract chemical concepts physically, and producing props that can be used in lectures or public outreach. A hands-on, creative project with real results you can hold.