README.md

metadata

title: MEIDNet Prism
emoji: 🔮
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 7860
pinned: true
license: mit
short_description: Learn, build and benchmark multimodal AI for materials
thumbnail: https://babu09-meidnet.hf.space/docs/assets/meidnet_prism_logo.png
tags:
  - materials
  - inverse-design
  - generative
  - crystal
  - perovskite
  - multimodal
  - contrastive-learning
  - chemistry
models:
  - Babu09/MEIDNet

MEIDNet Prism

Learn, build and benchmark multimodal AI for materials discovery. MEIDNet, the reference implementation, learns one shared latent space for crystal structures and their properties, enumerates a prototype-family design space checked by chemistry rules, and searches the latent space for candidates with the properties you want.

• Home — overview and a guided tour, from concept to demonstration.
• Learn — what a modality is, the five challenges of multimodal learning, contrastive learning with a playground, the Architecture Atlas with an advisor, how MEIDNet works, and the ecosystem it fits in.
• Benchmark — leaderboards under fixed protocols (inverse design, property prediction, representation on Perov-5, with baselines), metric families named as in LeMat-GenBench plus a conditional extension, meidnet score for any model's generated structures (compatibility), datasets and databases, and contributed results.
• Develop — the meidnet package (pip install meidnet), the Studio with the workflow Data → Model → Family → Rules → Targets → Search → Candidates, your own data, recipes, the configuration file, the Python API.
• MEIDNet Matter ↗ — the companion application for researchers: bring a dataset, set a design goal, read whether the data and model support it, search for candidates with evidence next to each one, export them with a validation ladder.

Model: Babu09/MEIDNet (the pretrained Perov-5 checkpoints) · Code: github.com/ABnano/MEIDNet (MIT) · Paper: A. Babu, R. A. Gouvêa, P. Vandergheynst, G.-M. Rignanese, npj Computational Materials (2026), doi:10.1038/s41524-026-02153-3.

Further reading

1. Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives, IEEE TPAMI 35, 1798–1828 (2013), doi:10.1109/TPAMI.2013.50.
2. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521, 436–444 (2015), doi:10.1038/nature14539.
3. B. Sanchez-Lengeling, A. Aspuru-Guzik, Inverse molecular design using machine learning: generative models for matter engineering, Science 361, 360–365 (2018), doi:10.1126/science.aat2663.
4. A. Babu, R. Almeida Gouvêa, G.-M. Rignanese, Toward automated discovery with generative models multimodal learning and closed loop workflows in inverse materials design, Cell Reports Physical Science 7, 103561 (2026), doi:10.1016/j.xcrp.2026.103561.
5. A. Babu, N. M. A. Krishnan, Multimodal and cross-modal learning techniques, APL Machine Learning 4, 030901 (2026), doi:10.1063/5.0346744.

This public Space is shared: uploads are capped (8 MB, 1,500 rows, 30 epochs), one training at a time, sessions are private per browser tab and removed after an hour. Predicted properties are the model's estimates — confirm candidates by DFT or experiment.