Scope and roadmap

What works today

Structures	any CIF with up to max_sites atoms (default 20); any elements
Properties	any number of scalar columns
Training	your data, your properties; the published settings by default
Generation	prototype families: a fixed arrangement of sites whose occupants and cell size are chosen. Built in: cubic ABX₃, A₂BB′X₆; write your own in YAML
Rules	charge balance, tolerance and octahedral factors, bond windows, minimum distance, predicted-property windows, element filters, and your own Python rules
Explanations	reports for data, training and generation; the Studio for live what-if

What does not work (yet)

• New atomic arrangements. The decoder's positions are not used to create geometry; candidates are compositions placed on the family prototype. Generating free geometry needs a decoder trained to produce it (the published decoder receives the true positions during training).
• Spectra, images, text as modalities. The property modality is a vector of scalars. Binned XRD or DOS could be added as a vector modality with its own encoder; it is designed for but not implemented.
• Large cells. max_sites can be raised, but memory and time grow with the square of it, and the published model was trained on 5-atom cells.
• Fine-tuning the published model on new properties. Its property head has two outputs; train a new model.

Roadmap (in order of likely value)

1. Vector modalities (XRD, DOS) with a shared-latent encoder each.
2. A training.init_from option to continue training from a checkpoint with the same properties.
3. A geometry-generating decoder variant (trained from prototype positions instead of true positions — model.decoder_coordinate_input: prototype is the first step and is available as an experimental setting).
4. More built-in families (spinel, Heusler, rocksalt/zincblende, MXene M₂XT₂).

Contributions of family files and rules are the easiest way to help; see the GitHub repository.