pymatviz 0.16.0

A toolkit for visualizations in materials informatics

pymatviz

A toolkit for visualizations in materials informatics.

If you use pymatviz in your research, see how to cite. Check out 23 existing papers using pymatviz for inspiration!

Installation

pip install pymatviz

See pyproject.toml for available extras like pip install 'pymatviz[brillouin]' to render 3d Brillouin zones.

API Docs

See the /api page.

Usage

See the Jupyter notebooks under examples/ for how to use pymatviz. PRs with additional examples are welcome! 🙏

matbench_dielectric_eda.ipynb		Launch Codespace
mp_bimodal_e_form.ipynb		Launch Codespace
matbench_perovskites_eda.ipynb		Launch Codespace
mprester_ptable.ipynb		Launch Codespace

Periodic Table

See pymatviz/ptable/ptable_plotly.py. The module supports heatmaps, heatmap splits (multiple values per element), histograms, scatter plots and line plots. All visualizations are interactive through Plotly and support displaying additional data on hover.

[!WARNING] Version 0.16.0 of pymatviz dropped the matplotlib-based functions in ptable_matplotlib.py in https://github.com/janosh/pymatviz/pull/270. Please use the plotly-based functions shown below instead which have feature parity, interactivity and better test coverage.

ptable_heatmap_plotly(atomic_masses)	ptable_heatmap_plotly(compositions, log=True)
ptable_hists_plotly(data)	ptable_scatter_plotly(data, mode="markers")
ptable_heatmap_splits_plotly(2_vals_per_elem)	ptable_heatmap_splits_plotly(3_vals_per_elem)

Dash app using ptable_heatmap_plotly()

See examples/mprester_ptable.ipynb.

https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4

Phonons

phonon_bands(bands_dict)	phonon_dos(doses_dict)
phonon_bands_and_dos(bands_dict, doses_dict)	phonon_bands_and_dos(single_bands, single_dos)

Composition Clustering

cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=2)	cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=3)

Visualize 2D or 3D relationships between compositions and properties using multiple embedding and dimensionality reduction techniques:

Embedding methods: One-hot encoding of element fractions, Magpie features (elemental properties), Matscholar element embeddings, MEGNet element embeddings

Dimensionality reduction methods: PCA (linear), t-SNE (non-linear), UMAP (non-linear), Isomap (non-linear), Kernel PCA (non-linear)

Example usage:

import pymatviz as pmv
from pymatgen.core import Composition

compositions = ("Fe2O3", "Al2O3", "SiO2", "TiO2")

# Create embeddings
embeddings = pmv.cluster.composition.one_hot_encode(compositions)
comp_emb_map = dict(zip(compositions, embeddings, strict=True))

# Plot with optional property coloring
fig = pmv.cluster_compositions(
    compositions=comp_emb_map,
    properties=[1.0, 2.0, 3.0, 4.0],  # Optional property values
    prop_name="Property",  # Optional property label
    embedding_method="one-hot",  # or "magpie", "matscholar_el", "megnet_el", etc.
    projection_method="pca",  # or "tsne", "umap", "isomap", "kernel_pca", etc.
    show_chem_sys="shape",  # works best for small number of compositions; "color" | "shape" | "color+shape" | None
    n_components=2,  # or 3 for 3D plots
)
fig.show()

Structure Clustering

On the roadmap but no ETA yet.

Structure

See pymatviz/structure_viz/plotly.py.

structure_3d_plotly(hea_structure)	structure_3d_plotly(lco_supercell)
structure_2d_plotly(six_structs)	structure_3d_plotly(six_structs)

Brillouin Zone

See pymatviz/brillouin.py.

brillouin_zone_3d(cubic_struct)	brillouin_zone_3d(hexagonal_struct)
brillouin_zone_3d(monoclinic_struct)	brillouin_zone_3d(orthorhombic_struct)

X-Ray Diffraction

See pymatviz/xrd.py.

xrd_pattern(pattern)	xrd_pattern({key1: patt1, key2: patt2})
xrd_pattern(struct_dict, stack="horizontal")	xrd_pattern(struct_dict, stack="vertical")

Radial Distribution Functions

See pymatviz/rdf/plotly.py.

element_pair_rdfs(pmg_struct)	element_pair_rdfs({"A": struct1, "B": struct2})

Coordination

See pymatviz/coordination/plotly.py.

coordination_hist(struct_dict)	coordination_hist(struct_dict, by_element=True)
coordination_vs_cutoff_line(struct_dict, strategy=None)	coordination_vs_cutoff_line(struct_dict, strategy=None)

Sunburst

See pymatviz/sunburst.py.

spacegroup_sunburst([65, 134, 225, ...])	chem_sys_sunburst(["FeO", "Fe2O3", "LiPO4", ...])

Treemap

See pymatviz/treemap/chem_sys.py.

chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...])	chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...], group_by="formula")
py_pkg_treemap("pymatviz")	py_pkg_treemap(["pymatviz", "torch_sim", "pymatgen"])

Rainclouds

See pymatviz/rainclouds.py.

rainclouds(two_key_dict)	rainclouds(three_key_dict)

Sankey

See pymatviz/sankey.py.

sankey_from_2_df_cols(df_perovskites)	sankey_from_2_df_cols(df_space_groups)

Bar Plots

See pymatviz/bar.py.

spacegroup_bar([65, 134, 225, ...], backend="plotly")	spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")

Histograms

See pymatviz/histogram.py.

elements_hist(compositions, log=True, bar_values='count')	histogram({'key1': values1, 'key2': values2})

Scatter Plots

See pymatviz/scatter.py.

density_scatter_plotly(df, x=x_col, y=y_col, ...)	density_scatter_plotly(df, x=x_col, y=y_col, ...)
density_scatter(xs, ys, ...)	density_scatter_with_hist(xs, ys, ...)
density_hexbin(xs, ys, ...)	density_hexbin_with_hist(xs, ys, ...)

Uncertainty

See pymatviz/uncertainty.py.

qq_gaussian(y_true, y_pred, y_std)	qq_gaussian(y_true, y_pred, y_std: dict)
error_decay_with_uncert(y_true, y_pred, y_std)	error_decay_with_uncert(y_true, y_pred, y_std: dict)

Classification

See pymatviz/classify/confusion_matrix.py.

confusion_matrix(conf_mat, ...)	confusion_matrix(y_true, y_pred, ...)

See pymatviz/classify/curves.py.

roc_curve_plotly(targets, probs_positive)	precision_recall_curve_plotly(targets, probs_positive)

How to cite pymatviz

See citation.cff or cite the Zenodo record using the following BibTeX entry:

@software{riebesell_pymatviz_2022,
  title = {Pymatviz: visualization toolkit for materials informatics},
  author = {Riebesell, Janosh and Yang, Haoyu and Goodall, Rhys and Baird, Sterling G.},
  date = {2022-10-01},
  year = {2022},
  doi = {10.5281/zenodo.7486816},
  url = {https://github.com/janosh/pymatviz},
  note = {10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz},
  urldate = {2023-01-01}, % optional, replace with your date of access
  version = {0.8.2}, % replace with the version you use
}

Papers using pymatviz

Sorted by number of citations, then year. Last updated 2025-03-25. Auto-generated from Google Scholar. Manual additions via PR welcome.

1. C Zeni, R Pinsler, D Zügner et al. (2023). Mattergen: a generative model for inorganic materials design (cited by 123)
2. J Riebesell, REA Goodall, P Benner et al. (2023). Matbench Discovery--A framework to evaluate machine learning crystal stability predictions (cited by 45)
3. C Chen, DT Nguyen, SJ Lee et al. (2024). Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation (cited by 36)
4. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). Open materials 2024 (omat24) inorganic materials dataset and models (cited by 31)
5. M Giantomassi, G Materzanini (2024). Systematic assessment of various universal machine‐learning interatomic potentials (cited by 15)
6. AA Naik, C Ertural, P Benner et al. (2023). A quantum-chemical bonding database for solid-state materials (cited by 12)
7. K Li, AN Rubungo, X Lei et al. (2025). Probing out-of-distribution generalization in machine learning for materials (cited by 8)
8. N Tuchinda, CA Schuh (2025). Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles (cited by 2)
9. A Onwuli, KT Butler, A Walsh (2024). Ionic species representations for materials informatics (cited by 2)
10. A Kapeliukha, RA Mayo (2025). MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations (cited by 1)
11. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). A Foundational Potential Energy Surface Dataset for Materials
12. Fei Shuang, Zixiong Wei, Kai Liu et al. (2025). Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
13. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). MatterChat: A Multi-Modal LLM for Material Science
14. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). Siamese Foundation Models for Crystal Structure Prediction
15. A Peng, MY Guo (2025). The OpenLAM Challenges
16. F Therrien, JA Haibeh (2025). OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
17. N Tuchinda, CA Schuh (2025). A Grain Boundary Embrittlement Genome for Substitutional Cubic Alloys
18. K Yan, M Bohde, A Kryvenko (2025). A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
19. Daniel W. Davies, Keith T. Butler, Adam J. Jackson et al. (2024). SMACT: Semiconducting Materials by Analogy and Chemical Theory
20. Hui Zheng, Eric Sivonxay, Rasmus Christensen et al. (2024). The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity
21. HH Li, Q Chen, G Ceder (2024). Voltage Mining for (De) lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage
22. Janosh Riebesell, Ilyes Batatia, Philipp Benner et al. (2023). A foundation model for atomistic materials chemistry
23. Jack Douglas Sundberg (2022). A New Framework for Material Informatics and Its Application Toward Electride-Halide Material Systems