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5-minute quickstart

Open the live Studio. It runs the published perovskite model: move rule limits and targets and watch what passes, run a latent search and read each candidate's checklist, export the meidnet.yaml. (Search budgets are capped on the shared server; the local install below has no limits and takes your own data.)

pip install meidnet          # PyTorch CPU wheels are fine; CUDA is optional
meidnet demo                 # halide perovskites, band gap 2.0 eV → runs/demo/

demo downloads the 2.8 MB published checkpoint, searches for 3 candidates (about a minute on a CPU), checks them against the family's rules and opens generation_report.html.

meidnet demo --family oxide --band-gap 3.0 --enthalpy -0.2 -n 5
meidnet studio               # the live workbench with the same model

Notebook 01 — quickstart

What you just saw

  1. Targets → latent. The property encoder maps (band gap, enthalpy) to a point in the shared latent space.
  2. Search. A population of latents near that point is optimised so that the decoder's output matches the targets and the family's soft terms (cubic cell, sensible charges, …).
  3. Decode + rules. Each latent is decoded into one element per site; the composition is placed on the family's prototype and must pass every hard rule (charge balance, tolerance factor, …).
  4. Report. The best new, unique candidates are saved as CIFs with a card that shows each rule's value and a gauge of predicted vs target property.

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