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MEIDNet Prism

MEIDNet Prism

← MEIDNet Prism home · the documentation of the MEIDNet framework and its platform.

Learn, build and benchmark multimodal AI for materials discovery. MEIDNet is the reference implementation: it designs crystalline materials from target properties, with your own data, your own rules and no code to edit.

   Learn ──► Architectures ──► Build ──► Datasets ──► Benchmarks ──► Community
   what is   which design      MEIDNet    data that    results with   share and
   multimodal fits your data   Studio     works        evidence       reproduce

MEIDNet learns one latent space shared by crystal structures and their properties, then searches it for new materials that hit property targets while obeying the chemical and structural rules of a material family. The published cubic-perovskite model (band gap + formation enthalpy) is one application of it; the framework accepts any table of structures and scalar properties and any prototype family you describe.

MEIDNet in one minute: data, model, family, rules, targets, search and candidates. The same tour opens on a first visit to the live Studio (the “▶ How it works” button replays it).

  • Learn multimodality

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

    Learn

  • Choose an architecture

    Early fusion, late fusion, shared latent spaces, cross-attention and contrastive learning, and an advisor that recommends one for your data and goal.

    Architecture Atlas

  • Try MEIDNet

    See what it does in 30 seconds: the live Studio needs nothing installed; meidnet demo runs on any laptop.

    5-minute quickstart

  • Use MEIDNet on my data

    A table with an id, properties and CIFs is all you need. Four commands take you from "is my data usable?" to candidates with explanations.

    Bring your own dataset

  • Change what it does

    Targets, elements, rules, family — all in one YAML file, or by moving sliders in MEIDNet Studio and exporting the file.

    Recipes

  • Understand it

    How the two modalities are aligned, how the search works, what the reports mean, and where the limits are.

    How MEIDNet works

The workflow

   Data ──► Model ──► Family ──► Rules ──► Targets ──► Search ──► Candidates
   table    shared    prototype  hard      property   latent     CIFs + report
   + CIFs   latent    + sites    checks    goals      optimisation

Each block is a setting in meidnet.yaml, a node in MEIDNet Studio — where changing one block immediately shows its effect on the ones after it — and a section in the HTML report each step writes.

Honest scope

  • Generation works for prototype families: a fixed arrangement of sites whose occupants and cell size are chosen (ABX₃ perovskites, A₂BB′X₆ double perovskites, and anything you describe the same way). MEIDNet does not invent new atomic arrangements. Details and roadmap.
  • Predicted properties are the model's estimates. The reports say when a prediction is an extrapolation. Confirm candidates with DFT or experiment; meidnet screen is a first filter.

Paper

A. Babu, R. Almeida Gouvêa, P. Vandergheynst, G.-M. Rignanese, MEIDNet: Multimodal generative AI framework for inverse materials design, npj Computational Materials (2026). Citation and benchmarks · code as published (v1.0).