The ecosystem¶
MEIDNet Prism and MEIDNet Matter do two jobs, and connect to what already exists rather than repeating it.
| MEIDNet Prism | MEIDNet Matter | |
|---|---|---|
| Job | Learn a method, reproduce it, compare it, benchmark it, implement it. | Bring a dataset and answer "what material should I investigate next?" |
| You come here to | read how multimodal learning works, run the published model, score a model against a fixed protocol, contribute a result, use the meidnet package. |
set property targets and chemistry rules, read whether the data and model support them, search for candidates, read the evidence next to each one, export CIFs and a run bundle. |
| Address | babu09-meidnet.hf.space | babu09-meidnet-matter.hf.space |
What MEIDNet works with¶
Data. Materials Project, NOMAD, the datasets on the datasets page and the 31 databases by application, and your own tables of structures and properties. Matter reads a table with CIF files; imports from Materials Project and NOMAD are planned.
Methods. MEIDNet (this package) and any other model: the benchmarks score the predictions or candidate structures of any method with the same code, and meidnet score reports the generation quality of any folder of CIF files (benchmark compatibility). Matter runs MEIDNet as its first backend behind one backend interface.
Benchmarks. The MEIDNet protocols per dataset, and metric families named as in LeMat-GenBench so that results can be read side by side, with MEIDNet's conditional extension for property-conditioned generation.
Synthesis knowledge. Not here: LeMat-Synth and the Materials Project Synthesis Explorer hold the recipes; Matter will link a candidate to them rather than extract its own.
Validation. Machine-learned potentials (MACE through meidnet screen), DFT, and experiment. Matter records each candidate's stage on a validation ladder; the benchmarks count DFT-confirmed hits separately from predicted ones.
The records that travel between them¶
A candidate leaves Matter as a JSON record (structure, targets, predicted values, domain status, rules, novelty, stability stage, model, dataset, provenance) and as a CIF file. meidnet score reads a folder of such CIF files with their targets.csv, so a Matter run can be scored on Prism with the same metrics as any other generator's output, and a scored set can go on to MLIP screening, DFT or an experiment.