Datasets

Datasets that work with MEIDNet, and what has been done with each. One card per dataset, never one score across datasets: the tasks, sizes and properties differ, so results are only compared within a dataset (see Benchmarks).

dataset	structures	properties used	size	with MEIDNet
Perov-5	cubic ABX₃ perovskites, 5 atoms per cell	direct band gap, formation enthalpy	18,928	trained model, configuration, tutorial, benchmark
MP-20	Materials Project, up to 20 atoms	none aligned in the paper	about 45,000	structure representation, published
Carbon-24	carbon allotropes, up to 24 atoms	none aligned in the paper	about 10,000	structure representation, published
Double perovskites A₂BB′X₆	a family file, no dataset	none	10,800 compositions (halide variant)	design space and rules only
Your dataset	one material family	any scalar columns	any	supported

New to multimodal data? Learn multimodality explains what a modality is, and Databases by application lists 31 computed and experimental sources.

• Perov-5 — published multimodal benchmark

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  18,928 cubic ABX₃ perovskites (CDVAE split: 11,356 train / 3,787 validation / 3,785 test), each with a relaxed structure, formation enthalpy (heat_all, eV/atom) and direct band gap (dir_gap, eV).

  Modalities used in MEIDNet: crystal structure · electronic property (band gap) · thermodynamic property (formation enthalpy).

  What the paper does with it: multimodal alignment of the structure and property encoders, property reconstruction, and the inverse-design demonstration (candidates generated from property targets, screened with MACE and validated by DFT).

  In this repository: meidnet download-data fetches it; the published checkpoint checkpoints/dual_autoencoder_clip_earlyfusion_propertyaware_2k.pth was trained on it; it is the data behind the live Studio.

  Suitable tasks: inverse design from a band gap and a stability target · property prediction from the structure · structure–property retrieval · measuring how well two modalities align.

  MEIDNet implementation: ✓ pretrained model · ✓ configuration (examples/perov5/meidnet.yaml) · ✓ tutorial (the paper's experiment, Colab) · ✓ benchmark results.

  Open in the Studio

  Benchmark results · The paper's experiment · Dataset source (CDVAE) · Castelli et al. 2012, Xie et al. 2022

• MP-20 — published: structure-representation generalization

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  ~45,000 structures from the Materials Project with at most 20 atoms per cell (CDVAE split).

  Published MEIDNet use: generalization of the crystal encoder / decoder beyond perovskites (structure representation and reconstruction). It is not a multimodal benchmark in the paper: no property modality was aligned on it, and no MP-20 checkpoint is shipped here.

  Benchmark results · Dataset source (CDVAE)

• Carbon-24 — published: structure-representation generalization

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  ~10,000 carbon structures with up to 24 atoms per cell (CDVAE split).

  Published MEIDNet use: the same structure-representation test as MP-20. Not a multimodal benchmark; no Carbon-24 checkpoint is shipped here.

  Benchmark results · Dataset source (CDVAE)

Your dataset

Use your dataset in the Studio

Any table with an id, one or more scalar property columns and a crystal structure per row (CIF text in a column or one .cif file per id) can be used directly: upload it in the Studio or run meidnet init → check → train → generate (bring your own dataset). The structures must belong to one material family (prototype + site groups), which is what the rules and the search need.

Datasets we would like to see benchmarked

Battery conductors, MOFs, 2D materials and spinels are natural next families. If you run MEIDNet on one of them, contribute the result: the dataset gets its own benchmark page, and a verified row once the result has been reproduced here.