| ---
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| title: MEIDNet Prism
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| emoji: 🔮
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| colorFrom: indigo
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| colorTo: purple
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| sdk: docker
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| app_port: 7860
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| pinned: true
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| license: mit
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| short_description: Learn, build and benchmark multimodal AI for materials
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| thumbnail: https://babu09-meidnet.hf.space/docs/assets/meidnet_prism_logo.png
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| tags: [materials, inverse-design, generative, crystal, perovskite, multimodal, contrastive-learning, chemistry]
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| models: [Babu09/MEIDNet]
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| ---
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|
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| <p align="center"><img src="https://babu09-meidnet.hf.space/docs/assets/meidnet_prism_logo.png" width="640" alt="MEIDNet Prism"></p>
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| # MEIDNet Prism
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| **Learn, build and benchmark multimodal AI for materials discovery.** MEIDNet, the reference
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| implementation, learns one shared latent space for crystal structures and their properties, enumerates a
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| prototype-family design space checked by chemistry rules, and searches the latent space for candidates
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| with the properties you want.
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| * **[Home](https://babu09-meidnet.hf.space/)** — overview and a guided tour, from concept to demonstration.
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| * **[Learn](https://babu09-meidnet.hf.space/docs/learn/index.html)** — what a modality is, the five
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| challenges of multimodal learning, contrastive learning with a playground, the
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| [Architecture Atlas](https://babu09-meidnet.hf.space/docs/learn/architectures.html) with an advisor,
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| how MEIDNet works, and [the ecosystem](https://babu09-meidnet.hf.space/docs/ecosystem.html) it fits in.
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| * **[Benchmark](https://babu09-meidnet.hf.space/docs/benchmarks/index.html)** — leaderboards under fixed
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| protocols (inverse design, property prediction, representation on Perov-5, with baselines), metric
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| families named as in LeMat-GenBench plus a conditional extension, `meidnet score` for any model's
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| generated structures ([compatibility](https://babu09-meidnet.hf.space/docs/benchmarks/compatibility.html)),
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| datasets and databases, and [contributed results](https://babu09-meidnet.hf.space/docs/community/contribute.html).
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| * **[Develop](https://babu09-meidnet.hf.space/docs/develop.html)** — the `meidnet` package (`pip install meidnet`),
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| the [Studio](https://babu09-meidnet.hf.space/studio/) with the workflow *Data → Model → Family → Rules →
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| Targets → Search → Candidates*, your own data, recipes, the configuration file, the Python API.
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| * **[MEIDNet Matter ↗](https://babu09-meidnet-matter.hf.space/)** — the companion application for
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| researchers: bring a dataset, set a design goal, read whether the data and model support it, search for
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| candidates with evidence next to each one, export them with a validation ladder.
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| **Model:** [Babu09/MEIDNet](https://huggingface.co/Babu09/MEIDNet) (the pretrained Perov-5 checkpoints) ·
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| **Code:** [github.com/ABnano/MEIDNet](https://github.com/ABnano/MEIDNet) (MIT) ·
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| **Paper:** A. Babu, R. A. Gouvêa, P. Vandergheynst, G.-M. Rignanese, *npj Computational Materials* (2026),
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| [doi:10.1038/s41524-026-02153-3](https://doi.org/10.1038/s41524-026-02153-3).
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|
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| **Further reading**
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| 1. Y. Bengio, A. Courville, P. Vincent, *Representation learning: a review and new perspectives*, IEEE TPAMI 35,
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| 1798–1828 (2013), [doi:10.1109/TPAMI.2013.50](https://doi.org/10.1109/TPAMI.2013.50).
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| 2. Y. LeCun, Y. Bengio, G. Hinton, *Deep learning*, Nature 521, 436–444 (2015),
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| [doi:10.1038/nature14539](https://doi.org/10.1038/nature14539).
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| 3. B. Sanchez-Lengeling, A. Aspuru-Guzik, *Inverse molecular design using machine learning: generative models for
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| matter engineering*, Science 361, 360–365 (2018), [doi:10.1126/science.aat2663](https://doi.org/10.1126/science.aat2663).
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| 4. A. Babu, R. Almeida Gouvêa, G.-M. Rignanese, *Toward automated discovery with generative models multimodal
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| learning and closed loop workflows in inverse materials design*, Cell Reports Physical Science 7, 103561 (2026),
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| [doi:10.1016/j.xcrp.2026.103561](https://doi.org/10.1016/j.xcrp.2026.103561).
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| 5. A. Babu, N. M. A. Krishnan, *Multimodal and cross-modal learning techniques*, APL Machine Learning 4, 030901
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| (2026), [doi:10.1063/5.0346744](https://doi.org/10.1063/5.0346744).
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| This public Space is shared: uploads are capped (8 MB, 1,500 rows, 30 epochs), one training at a time,
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| sessions are private per browser tab and removed after an hour. Predicted properties are the model's
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| estimates — confirm candidates by DFT or experiment.
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|
