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

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.

Keep one exercise open at a time. Each of these pages carries its own Python runtime and uses about 1.5 GB of memory, so close the tab for the exercise you have finished before opening the next. Two open at once is fine on most laptops; three is not.

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.toml

Optional: run the ACE workbench locally too. If you have Docker, you can do the fitting on your own machine — useful if the hosted workbench is busy or unreachable, and it is how you keep using the workbench after the school.

curl -O https://mlipschool.uk/local.yml
docker compose -f local.yml up -d

Then set the notebook's ACE workbench field to http://localhost:8002 and leave Oracle as it is: labels still come from the hosted oracle, so your label budget still applies. This works with the notebook you run yourself, not with one opened from this site.

You 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.