Predict Catalyst Properties for a Green-Hydrogen Pharma Spinout
Overview
What this challenge is about.
Train a model with uncertainty on open catalyst data and build a CSV tool. You get a verifiable certificate.
The scenario
The spinout (around 25 chemists, post-Series A) is in a race against several European competitors on cost-per-kilogram of green hydrogen; a 10x reduction in failed-synthesis attempts per quarter would meaningfully extend the cash runway.
The Brief
What you'll do, and what you'll demonstrate.
Ship a ranking tool that prioritizes catalyst candidates for synthesis using a calibrated ML model.
Earning criteria — what you'll demonstrate
- Apply tabular ML to a real scientific dataset with chemistry descriptors
- Quantify and calibrate predictive uncertainty for ranking decisions
- Translate a model into a tool a non-ML bench scientist can run
- Communicate ranking quality with metrics chemists understand
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI for Science and Engineering
Master · Applied Ai
Strong alignment
This challenge maps to AI for Science and Engineering at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Tabular Modeling
Apply tabular modeling to solve real industry problems and demonstrate production-level capability.
- Uncertainty Quantification
Apply uncertainty quantification to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Scientific Ml
Apply scientific ml to solve real industry problems and demonstrate production-level capability.
- Ranking Evaluation
Apply ranking evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Owning a tabular regression model end-to-end for a domain audience is the daily reality of a data scientist embedded in an R-and-D team.
This challenge sharpens
- tabular-modeling
- feature-engineering
- ranking-evaluation
Applied AI Scientist
Quantifying uncertainty for ranking decisions in a scientific context is exactly the applied-AI bridge into a chemistry or materials team.
This challenge sharpens
- uncertainty-quantification
- scientific-ml
- ranking-evaluation
Machine Learning Engineer
Packaging the model behind a CLI tool with clean inputs and outputs is the MLE's productionization craft.
This challenge sharpens
- python
- tabular-modeling
- feature-engineering