MEIDNet Prism

MEIDNet Prism

Learn, build and benchmark multimodal AI for materials discovery

From what a modality is to a trained model and a reproducible result. MEIDNet is the reference implementation: one space shared by crystal structures and their properties, searched for new materials with the properties you want.

▶ Try MEIDNetUse your datasetLearn multimodality

Published in npj Computational Materials (2026) · doi 10.1038/s41524-026-02153-3 · GitHub · MIT licence

MEIDNet Prism: several modalities enter a prism and leave it as one connected representation of materials.
MEIDNet in one minute: data → model → family → rules → targets → search → candidates. The same tour opens on your first visit to the Studio.

Six connected layers

Learn the ideas, pick a design, build it on your data, and show the evidence. Each layer links to the next.

Learn→ Architectures→ Build→ Datasets→ Benchmarks→ Community

1 · LearnWhat is multimodal learning?

Modalities, the five challenges of multimodal learning, contrastive learning with a playground, and an interactive map of materials modalities.

Learn multimodality →

2 · ArchitecturesWhich design fits my data?

Early and late fusion, shared latent spaces, cross-attention and contrastive learning, drawn side by side, with an advisor that recommends one.

Architecture Atlas →   Advisor →

3 · BuildMEIDNet Studio

Upload structures and properties, train in the browser, set targets and rules, run the search and inspect every candidate in 3D. No code; one YAML file.

Try MEIDNet →   Recipes by problem →

4 · DatasetsData that works

Perov-5, the published multimodal benchmark, other datasets with their status, and 31 computed and experimental databases grouped by application.

Datasets →   Databases →

5 · BenchmarksResults with evidence

One leaderboard per dataset, an evidence ladder from “generated” to “experimentally validated”, and rows re-run here marked verified.

Benchmarks →

6 · CommunityShare and reproduce

Submit a result through GitHub. The maintainer re-runs it and marks it verified when it reproduces; a run that does not agree is shown too.

Contribute a result →

Inside Build: the MEIDNet Studio in seven blocks

The reference implementation. Change one block and the blocks after it update.

Datayour materials
Modelshared space
Familycrystal type
Ruleschemistry
Targetswhat you want
Searchinverse design
Candidatesnew materials
Published in npj Computational MaterialsOpen source, MITBit-exact regression against the paper’s code Three pretrained checkpointsMLIP stability screening (MACE)Runs on a laptop CPU

Citation

A. Babu, R. A. Gouvêa, P. Vandergheynst and G.-M. Rignanese, MEIDNet: Multimodal generative AI framework for inverse materials design, npj Computational Materials (2026). doi:10.1038/s41524-026-02153-3 · arXiv:2601.22009

@article{meidnet2026,
  title   = {MEIDNet: Multimodal generative AI framework for inverse materials design},
  author  = {Anand Babu and Rog{\'e}rio Almeida Gouv{\^e}a and Pierre Vandergheynst and Gian-Marco Rignanese},
  journal = {npj Computational Materials},
  year    = {2026},
  doi     = {10.1038/s41524-026-02153-3}
}