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Reading the reports

Each command writes a self-contained HTML file in runs/<name>/. All three open with a one-sentence verdict (green / amber / red), show the evidence, and end with what to do next.

check_report.html

  • Verdict: how many rows are usable; the most common problem if many were skipped.
  • What MEIDNet read: table, columns, structure source, family and how many structures were re-ordered to the prototype.
  • Rows that were skipped: one row per reason with counts and example ids, and a "how do I fix these?" box.
  • Your properties: histogram, min/median/max, and warnings (e.g. "72 % of band-gap values are 0 — the model sees few examples of other values").
  • Your structures: atoms per cell, most common elements.

training_report.html

  • Verdict: are the properties predicted well from structure alone?
  • How accurate: per property, typical error (MAE), natural spread (std), R², and a word — good if the error is under a quarter of the spread, fair under a half, weak otherwise — with predicted-vs-true plots.
  • Did the two modalities align: retrieval accuracy (a structure's own property vector is its nearest match x % of the time) and the cosine curve. Inverse design depends on this.
  • Training curves and notes (e.g. the alignment ramp longer than training).

generation_report.html

  • Verdict: how many candidates, and whether any predicted value lies outside the training range.
  • Per target: the funnel (compositions alive after each rule — the biggest drop is the rule that limits the family most), the candidate cards, and examples of rejected compositions with the rule and the measured value.
  • A candidate card shows: elements per site, cell edge, round found, a gauge per objective (training range as a track, target as a tick, prediction as a dot), the checklist with each rule's value and allowed window (hover for the explanation), and warnings: prediction outside the training range, or a latent far from where training latents live (predictions less reliable).
  • Where the search went: latents before and after optimisation projected to two dimensions.

candidates.csv and generation.json

The CSV has one row per candidate with targets, predictions, elements, cell edge, every rule's value, score and file name. The JSON holds the full funnel counts, rejected examples and settings — everything the report shows.