MLIP School 2026 — Dataset craft for machine-learned interatomic potentials using ACEpotentials.jl
You will build a silicon dataset from scratch, fit ACE potentials, and learn why a low RMSE can still be a bad potential.
The opening talk is a five-minute read of the slides from the room, published flat so it keeps working after the session.
Two ways to work
In your browser
- E1: Oracle, first fit, and why RMSE is not the whole truth
- E1x: Choosing a basis (optional extension)
- E2: Surfaces — extrapolation bites, dataset curation fixes it
- E3: Automating curation — sampling versus selection
- C: The truth about the truth
Work through the day-one exercises in order: E1, E2, E3, then C. E1x is an optional extension — take it after E1 if you are ahead. It resumes nothing and spends none of your labels, so skipping it costs you nothing.
Day two — bring your own system
A separate one-hour session the following day. It resumes nothing from day one: you bring a system of your own and run the same loop on it.
On your own machine
Run:
uvx mograder student https://mlipschool.uk/mograder.tomlYou will need your API key. You were given one on a printed slip at registration. Paste it into the notebook's API key field, and use the same key throughout — E2, E3 and C resume the potential you fit in E1, and models belong to the key that fitted them.