pymatviz

A toolkit for visualizations in materials informatics. [](https://github.com/janosh/pymatviz/actions/workflows/test.yml) [](https://python.org/downloads) [](https://pypi.org/project/pymatviz) [](https://codecov.io/gh/janosh/pymatviz) [](https://pypistats.org/packages/pymatviz) [](https://zenodo.org/records/10456384)

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

Installation

sh 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! 🙏

| | | | | ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | | matbenchdielectriceda.ipynb | | Launch Codespace | | mpbimodale_form.ipynb | | Launch Codespace | | matbenchperovskiteseda.ipynb | | Launch Codespace | | mprester_ptable.ipynb | | Launch Codespace |

Periodic Table

See pymatviz/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:

```py import pymatviz as pmv from pymatgen.core import Composition

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

Create embeddings

embeddings = pmv.cluster.composition.onehotencode(compositions) compembmap = dict(zip(compositions, embeddings, strict=True))

Plot with optional property coloring

fig = pmv.clustercompositions( compositions=compembmap, properties=[1.0, 2.0, 3.0, 4.0], # Optional property values propname="Property", # Optional property label embeddingmethod="one-hot", # or "magpie", "matscholarel", "megnetel", etc. projectionmethod="pca", # or "tsne", "umap", "isomap", "kernelpca", etc. showchemsys="shape", # works best for small number of compositions; "color" | "shape" | "color+shape" | None ncomponents=2, # or 3 for 3D plots ) fig.show() ```

Structure Clustering

On the roadmap but no ETA yet.

Structure

See pymatviz/structure/plotly.py.

| structure_3d(hea_structure) | structure_3d(lco_supercell) | | :---------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: | | | | | structure_2d(six_structs) | structure_3d(six_structs) | | | |

Interactive Widgets

See pymatviz/widgets. Interactive 3D structure, molecular dynamics trajectory and composition visualization widgets for Jupyter, Marimo, and VSCode notebooks, powered by anywidget and MatterViz (https://github.com/janosh/matterviz). Supports pymatgen Structure, ASE Atoms, and PhonopyAtoms, as well as ASE, pymatgen and plain Python trajectory formats.

```py from pymatviz import StructureWidget, CompositionWidget, TrajectoryWidget from pymatgen.core import Structure, Composition

Interactive 3D structure visualization

structure = Structure.fromfile("structure.cif") structwidget = StructureWidget(structure=structure)

Interactive composition visualization

composition = Composition("Fe2O3") comp_widget = CompositionWidget(composition=composition)

Interactive trajectory visualization

trajectory1 = [struct1, struct2, struct3] # List of structures traj_widget1 = TrajectoryWidget(trajectory=trajectory1)

trajectory2 = [{"structure": struct1, "energy": 1.0}, {"structure": struct2, "energy": 2.0}, {"structure": struct3, "energy": 3.0}] # dicts with "structure" and property values traj_widget2 = TrajectoryWidget(trajectory=trajectory2) ```

Examples:

• Jupyter notebook demo
• Marimo demo
• VSCode interactive demo

[!TIP] Checkout the ✅ MatterViz VSCode extension for using the same viewers directly in VSCode/Cursor editor tabs for rendering local and remote files: marketplace.visualstudio.com/items?itemName=janosh.matterviz

Importing pymatviz auto-registers all widgets for their respective sets of supported objects via register_matterviz_widgets(). To customize the registration, use set_renderer().

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", ...]) | | :-----------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------: | | | | | chem_env_sunburst(single_struct) | chem_env_sunburst(multiple_structs) | | | |

Treemap

See pymatviz/treemap/chem_sys.py.

| chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...]) | chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...], group_by="formula") | | :----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | | | | | chem_env_treemap(structures) | chem_env_treemap(structures, max_cells_cn=3, max_cells_ce=4) | | | | | py_pkg_treemap("pymatviz") | py_pkg_treemap(["pymatviz", "torch_sim", "pymatgen"]) | | | | | py_pkg_treemap("pymatviz", color_by="coverage") | py_pkg_treemap("pymatgen", color_by="coverage", color_range=(0, 100)) | | | |

Note: For color_by="coverage" the package must have coverage data (e.g. run pytest --cov=<pkg> --cov-report=xml and pass the resulting .coverage file to coverage_data_file).

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, ...]) | spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...]) | | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------: | | | |

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:

bib @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 2025-05-07. 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 134)
2. J Riebesell, REA Goodall, P Benner et al. (2023). Matbench Discovery--A framework to evaluate machine learning crystal stability predictions (cited by 53)
3. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). Open materials 2024 (omat24) inorganic materials dataset and models (cited by 48)
4. 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 43)
5. M Giantomassi, G Materzanini (2024). Systematic assessment of various universal machine‐learning interatomic potentials (cited by 22)
6. AA Naik, C Ertural, P Benner et al. (2023). A quantum-chemical bonding database for solid-state materials (cited by 15)
7. K Li, AN Rubungo, X Lei et al. (2025). Probing out-of-distribution generalization in machine learning for materials (cited by 9)
8. 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 3)
9. N Tuchinda, CA Schuh (2025). Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles (cited by 2)
10. A Onwuli, KT Butler, A Walsh (2024). Ionic species representations for materials informatics (cited by 2)
11. A Peng, MY Guo (2025). The OpenLAM Challenges (cited by 1)
12. F Therrien, JA Haibeh (2025). OBELiX: A curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes (cited by 1)
13. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). A Foundational Potential Energy Surface Dataset for Materials
14. 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
15. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). MatterChat: A Multi-Modal LLM for Material Science
16. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). Siamese Foundation Models for Crystal Structure Prediction
17. K Yan, M Bohde, A Kryvenko (2025). A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
18. N Tuchinda, CA Schuh (2025). A Grain Boundary Embrittlement Genome for Substitutional Cubic Alloys
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