Goldilocks Data

Setting up a DFT calculation means choosing numerical inputs that are hard to choose well. A k-point mesh that is too coarse gives the wrong energy; one that is too dense wastes compute.

Goldilocks learns those choices from systematic convergence calculations. This site is where those data are made and documented.

Just want recommended inputs?

Then you want Goldilocks Core. Give it a structure and a calculation intent; it selects suitable inputs.

Read on if you want to reproduce a data campaign, inspect how convergence was labelled, or use the exported records.

What this repository does

1. Generate — submit parameter sweeps through AiiDA with stable structure identifiers and calculation provenance.
2. Analyse — apply explicit convergence criteria and find the smallest acceptable input for each structure.
3. Publish — export documented CSV and Parquet snapshots for research and model training.

Model training belongs in Goldilocks ML. End-user input generation belongs in Goldilocks Core.

Current data campaign

The first campaign measures Quantum ESPRESSO no-spin SCF k-point convergence with a gamma-inclusive kindex schedule. It compares PseudoDojo and SSSP PBEsol pseudopotentials.

The campaign extends an unconverged structure by three meshes at a time, records every calculation in AiiDA, and exports one summary row per structure.

Where to go

Install the environment Run the k-point campaign Use the results