Recipes by problem¶

Start from a research question. Each recipe goes problem → architecture → why → data → configuration → tutorial → references, and says plainly whether MEIDNet does it today. Where it does not, the recipe points to the kind of model that does.

For step-by-step changes to a MEIDNet configuration (another target, another family, a new rule), see the how-to guides.

problem	architecture	MEIDNet today
Perovskites with a target band gap that are likely to be stable	shared latent + contrastive	Supported
Candidates from your own DFT results for one family	shared latent + contrastive	Supported
Screen a whole family for a target before searching	structure encoder of the shared space	Supported
Identify the crystal structure from an XRD pattern	1D convolutional network; contrastive pattern ↔ structure	Planned
Predict a property when only the formula is known	composition network	Use another tool
Connect synthesis text with structures	text encoder + contrastive or cross-attention	Planned
Design new atomic arrangements	conditional generative model over full geometry	Use another tool

Perovskites with a target band gap that are likely to be stable¶

Supported in MEIDNet


Problem	Find cubic ABX₃ halide perovskites with a direct band gap near 1.5 eV and a negative formation enthalpy.
Architecture	Shared latent space with contrastive alignment: MEIDNet.
Why	The question runs from properties to structure. A shared space whose crystal decoder is also trained from the property latent alone gives that direction, and the family's rules keep the chemistry sensible.
Data	Perov-5: 18,928 structures with DFT band gap and formation enthalpy (dataset card). The published checkpoint is trained on it.
Tutorial	The paper's experiment · in the browser: Studio, Targets block · screen the candidates with MACE
References	Babu et al. 2026 (MEIDNet); Castelli et al. 2012 (the dataset).

One command with the published model:

meidnet demo --family halide --band-gap 1.5 --enthalpy -0.10

Or in meidnet.yaml (from examples/perov5/meidnet.yaml):

generation:
  family: perovskite_abx3
  variant: halide
  objectives:
    - {property: dir_gap,  loss: l2, weight: 10000, select_weight: 1.0}
    - {property: heat_all, loss: l1, weight: 6000,  select_weight: 0.4}
  targets:
    - {dir_gap: 1.5, heat_all: -0.10}

Candidates from your own DFT results for one family¶

Supported in MEIDNet


Problem	You computed structures and properties for one prototype family, for example A₂BB′X₆ double perovskites, and want new compositions with chosen property values.
Architecture	Shared latent space with contrastive alignment, trained on your table.
Why	The same reasoning as above. Every scalar column you give becomes a target you can set, and the family file says which sites exist and which elements may sit on them.
Data	A table with an id, numeric property columns and one CIF per row. Public sources by application are on Databases by application.
Tutorial	Bring your own dataset · in the browser: Studio, Data block · change the material family
References	Babu et al. 2026.

data:
  table: my_results.csv
  id_column: id
  cif_column: cif
  properties:
    - {column: band_gap, unit: eV}
    - {column: e_form,   unit: eV/atom}
generation:
  family: double_perovskite_a2bbx6
  variant: halide
  objectives:
    - {property: band_gap, loss: l2,      weight: 10000}
    - {property: e_form,   loss: at_most, weight: 6000}
  targets:
    - {band_gap: 1.8, e_form: -0.10}

Then meidnet check → meidnet train → meidnet generate; each step writes a report.

Screen a whole family for a target before searching¶

Supported in MEIDNet


Problem	Before running a search, see every composition the family allows, its rule values and its predicted properties, and which are predicted closest to the target.
Architecture	The structure encoder of the shared space, read in the forward direction (structure → properties).
Why	For a prototype family the set of compositions is finite, so it can be scored exhaustively in seconds. Comparing these predictions with the search's candidates is a useful sanity check.
Data	A trained model and a family; nothing else.
Tutorial	Explore in 3D · in the browser: Studio, design space · How MEIDNet works
References	Babu et al. 2026.

meidnet space examples/perov5/meidnet.yaml --model checkpoints/dual_autoencoder_clip_earlyfusion_propertyaware_2k.pth -o space.csv

Identify the crystal structure from an XRD pattern¶

Planned in MEIDNet


Problem	Given a powder diffraction pattern, tell which crystal system, space group or known structure it comes from.
Architecture	Today: a 1D convolutional classifier on the pattern (one modality). Multimodal: a pattern encoder aligned with a structure encoder by contrastive learning, so a pattern retrieves the structures it matches.
Why	A pattern is a fingerprint of the lattice. A classifier needs labels; a contrastive model needs only (pattern, structure) pairs, which can be simulated from any structure database.
Data	Simulate patterns from computed or experimental structures (semiconductor physics, all databases), for example with pymatgen's XRD calculator; validate on measured patterns.
In MEIDNet	A vector-modality encoder for binned XRD is the first item of the roadmap. It is not implemented.
References	Park et al. 2017; Baltrušaitis et al. 2019.

Predict a property when only the formula is known¶

Use another tool


Problem	Predict a band gap or a stability measure for compositions whose crystal structure is unknown, as in many experimental tables.
Architecture	A composition network that reads the formula as a weighted set of elements, such as Roost. With extra numeric inputs (temperature, doping), early fusion of composition features and those numbers is a strong baseline.
Why	MEIDNet needs a structure for every row. If your formulas all sit on one prototype (for example ABX₃), you can build those structures on the prototype and use MEIDNet; otherwise a composition model is the right tool.
Data	Experimental band gaps and Matbench tasks are listed on Databases by application.
References	Goodall and Lee 2020 (Roost); Dunn et al. 2020 (Matbench).

Connect synthesis text with structures¶

Planned in MEIDNet


Problem	Link written synthesis procedures to the structures they produce, to suggest how a candidate might be made or which candidates resemble known syntheses.
Architecture	A text encoder (a language model) aligned with a structure encoder by contrastive learning; cross-attention when individual words must be tied to individual elements or steps.
Why	Text and structures have no common format; a shared space lets each be compared with the other.
Data	Text-mined synthesis recipes (Kononova et al. 2019) paired with structures from a structure database.
In MEIDNet	A text encoder into the shared space is planned, not implemented.
References	Kononova et al. 2019; Moro, Loh et al. 2025.

Design new atomic arrangements¶

Use another tool


Problem	Generate crystals whose atomic arrangement is not one of a few known prototypes.
Architecture	A conditional generative model over the full geometry: diffusion (MatterGen) or a variational autoencoder (CDVAE).
Why	MEIDNet places compositions on a family prototype and does not invent arrangements (scope). Models that generate positions and cells directly can.
Data	Large structure databases; the generative benchmarks are listed under generative-model benchmarks.
References	Zeni et al. 2025 (MatterGen); Xie et al. 2022 (CDVAE).

References¶

A. Babu, R. Almeida Gouvêa, P. Vandergheynst, G.-M. Rignanese, "MEIDNet: Multimodal generative AI framework for inverse materials design", npj Comput. Mater. (2026). doi:10.1038/s41524-026-02153-3
I. E. Castelli et al., "New cubic perovskites for one- and two-photon water splitting using the computational materials repository", Energy Environ. Sci. 5, 9034 (2012). doi:10.1039/C2EE22341D
W. B. Park et al., "Classification of crystal structure using a convolutional neural network", IUCrJ 4, 486–494 (2017). doi:10.1107/S205225251700714X
R. E. A. Goodall, A. A. Lee, "Predicting materials properties without crystal structure: deep representation learning from stoichiometry", Nat. Commun. 11, 6280 (2020). doi:10.1038/s41467-020-19964-7
A. Dunn et al., "Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm", npj Comput. Mater. 6, 138 (2020). doi:10.1038/s41524-020-00406-3
O. Kononova et al., "Text-mined dataset of inorganic materials synthesis recipes", Sci. Data 6, 203 (2019). doi:10.1038/s41597-019-0224-1
C. Zeni et al., "A generative model for inorganic materials design", Nature 639, 624–632 (2025). doi:10.1038/s41586-025-08628-5
T. Xie et al., "Crystal diffusion variational autoencoder for periodic material generation", ICLR (2022). arXiv:2110.06197
T. Baltrušaitis, C. Ahuja, L.-P. Morency, "Multimodal machine learning: a survey and taxonomy", IEEE TPAMI 41, 423–443 (2019). doi:10.1109/TPAMI.2018.2798607
V. Moro, C. Loh et al., "Multimodal foundation models for material property prediction and discovery", Newton (2025). doi:10.1016/j.newton.2025.100016