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Paper

MEIDNet: Multimodal generative AI framework for inverse materials design. Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst, Gian-Marco Rignanese. 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}
}

Code and data of the paper

  • Code as published: tag v1.0.0-paper (frozen; MEIDNet 2 reproduces it bit for bit — see Perov-5).
  • Checkpoints: checkpoints/ in the repository (2.8 MB each).
  • Data: Perov-5 in the CDVAE split (meidnet download-data); Castelli et al., Energy Environ. Sci. 5, 5814 (2012); Xie et al., ICLR (2022).
  • Generated structures: examples/perov5/paper_results/.

Further reading

  • 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
  • A. Babu, N. M. A. Krishnan, Multimodal and cross-modal learning techniques, APL Machine Learning 4, 030901 (2026). doi:10.1063/5.0346744

Methods MEIDNet builds on

  • Satorras, Hoogeboom, Welling, E(n) equivariant graph neural networks, ICML 2021 (the encoder).
  • Radford et al., CLIP, ICML 2021 (the alignment objective).
  • Batatia et al., MACE-MP-0 (stability screening).