goldilocks-core

Goldilocks recommends settings for density functional theory (DFT) calculations and generates Quantum ESPRESSO input files (SCF, DOS, relaxation, and variable-cell relaxation) from a crystal structure.

Installation

Not published to PyPI yet -- clone the repository. Install uv first:

git clone https://github.com/junwen94/goldilocks-core.git
cd goldilocks-core
uv sync

Try it

Start the Workbench

With Node.js 24 or newer installed, run:

uv sync --extra http
npm --prefix web ci
uv run goldilocks assets install workbench
uv run --extra http poe workbench

The asset step installs the models and pseudopotential tables. Open http://127.0.0.1:5173, upload a CIF or POSCAR, review the recommended settings, and download the generated inputs.

For a built frontend instead, stop the development servers and run:

uv run --extra http poe stage

Then open http://127.0.0.1:8000. See the Workbench guide for Docker and development checks.

With mMACE (ML-backed magnetism features)

Without any extra setup, magnetism classification (is_magnetic) and magnetic-ordering ranking run at a heuristic/LLM tier. To get the real ML tier, install mace/e3nn/sphericart and a checkpoint file once, manually -- none of this can ever be a pip/uv extra (the mace fork it needs has no PyPI release):

uv pip install ase==3.28.0 e3nn==0.4.4 sphericart==1.0.9 sphericart-torch==1.0.9
uv pip install "mace-torch @ git+https://github.com/CheukHinHoJerry/mace.git@19cdf6692c48e068a24e06cfe1ffc670e8aea3dd"
mkdir -p ~/.local/share/goldilocks/mmace
curl -L -o ~/.local/share/goldilocks/mmace/mace_matpes_pbe_baseline_run-3.model \
  https://data-collections.psdi.ac.uk/api/records/1g8rw-q8128/files/mace_matpes_pbe_baseline_run-3.model/content
export GOLDILOCKS_MACE_BACKBONE=~/.local/share/goldilocks/mmace/mace_matpes_pbe_baseline_run-3.model

Then start the Workbench as above in the same shell (the backend only picks up GOLDILOCKS_MACE_BACKBONE if it's set before launch). Load a magnetic structure (e.g. src/goldilocks_core/examples/structures/Fe_bcc.cif) and check the Analysis column's "is magnetic" field: its caption switches to "Goldilocks-ML prediction" once the ml tier is live.

Two gotchas worth knowing up front: uv sync silently removes the two manually-installed packages again (they're not in uv.lock) -- re-run the uv pip install lines above after any uv sync; and the Workbench's own "Run mMACE" ranking button is currently broken (stfc/goldilocks-ml#95) -- use uv run goldilocks magnetic-orderings --rank-with-mmace from the CLI for ranking instead. Checksum verification and full troubleshooting: mMACE setup.

Generate inputs from the command line

Download the prediction models and default pseudopotential table, then generate inputs for the bundled silicon structure:

uv run goldilocks assets install default
uv run goldilocks run src/goldilocks_core/examples/structures/Si.cif --out si-run

Open si-run/scf.in to see the input. The directory also contains the pseudopotential file, a submission script, and goldilocks.json (full provenance for every setting).

Treat the recommended settings as a starting point: review warnings and check convergence for your calculation. The quickstart explains the output and how to run it.

Guides and reference

• First calculation — generate, check, and run an input.
• Python API — use Goldilocks in a script.
• Recommendations — understand the choices and their limits.
• Pseudopotentials — choose a table and understand automatic selection.
• CLI reference — commands and options.
• mMACE setup — enable the ML-backed magnetism features.
• Scientific conventions — units and numerical definitions.
• Contributing — code layout and development checks.

Licence

Code: BSD 3-Clause. Documentation under docs/ and example structures: CC BY 4.0. Downloaded pseudopotentials retain their upstream licences.