pymatviz

A toolkit for visualizations in materials informatics.

If you use pymatviz in your research, see how to cite. Check out 41 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/figures.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 #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.

2022-07-28-ptable_heatmap_plotly-dash-example.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/figures.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.

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

# Interactive 3D structure visualization
structure = Structure.from_file("structure.cif")
struct_widget = 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().

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/figures.py.

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

Coordination

See pymatviz/coordination/figures.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", "flame", "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(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 2026-02-25. Auto-generated from Google Scholar. Manual additions via PR welcome.

1. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). Open materials 2024 (omat24) inorganic materials dataset and models (cited by 230)
2. C Zeni, R Pinsler, D Zügner et al. (2023). Mattergen: a generative model for inorganic materials design (cited by 166)
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 131)
4. J Riebesell, REA Goodall, P Benner et al. (2023). Matbench Discovery--A framework to evaluate machine learning crystal stability predictions (cited by 106)
5. H Yu, M Giantomassi, G Materzanini (2024). Systematic assessment of various universal machine‐learning interatomic potentials (cited by 69)
6. M Gibaldi, A Kapeliukha, A White et al. (2025). MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations (cited by 34)
7. AA Naik, C Ertural, N Dhamrait et al. (2023). A quantum-chemical bonding database for solid-state materials (cited by 28)
8. F Therrien, J Abou Haibeh, D Sharma et al. (2026). OBELiX: A curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes (cited by 9)
9. K Li, AN Rubungo, X Lei et al. (2024). Probing out-of-distribution generalization in machine learning for materials (cited by 9)
10. Y Zhou, X He, Z Li (2025). Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning (cited by 6)
11. HH Li, Q Chen, G Ceder (2024). Voltage Mining for (De) lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage (cited by 5)
12. A Peng, X Liu, MY Guo et al. (2025). The openlam challenges (cited by 3)
13. J Nam, S Liu, G Winter et al. (2025). Flow matching for accelerated simulation of atomic transport in crystalline materials (cited by 3)
14. A Onwuli, KT Butler, A Walsh (2024). Ionic species representations for materials informatics (cited by 3)
15. N Tuchinda, CA Schuh (2025). Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles (cited by 2)
16. R Nduma, H Park, A Walsh (2025). Crystalyse: a multi-tool agent for materials design (cited by 2)
17. N Tuchinda, CA Schuh (2025). A grain boundary embrittlement genome for substitutional cubic alloys (cited by 1)
18. A Giunto, Y Fei, P Nevatia et al. (2025). Harnessing Automated SEM-EDS and Machine Learning to Unlock High-Throughput Compositional Characterization of Powder Materials (cited by 1)
19. T Cavignac, J Schmidt, PP De Breuck et al. (2025). AI-Driven Expansion and Application of the Alexandria Database (cited by 1)
20. J Riebesell, H Yang, R Goodall et al. (2024). janosh/pymatviz: v0. 11.0 (cited by 1)
21. T Warford, FL Thiemann, GĂĄ CsĂĄnyi (2026). Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
22. Mohammed Al-Fahdi, Riccardo Rurali, Jianjun Hu et al. (2025). Accelerated discovery of extreme lattice thermal conductivity by crystal graph attention networks and chemical bonding
23. Omar Allam, Brook Wander, SungYeon Kim et al. (2025). AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
24. Giulio Benedini, Antoine Loew, Matti Hellstrom et al. (2025). Universal Machine Learning Potential for Systems with Reduced Dimensionality
25. Yuan Chiang, Tobias Kreiman, Elizabeth Weaver et al. (2025). MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform
26. Orion Archer Cohen, Janosh Riebesell, Rhys Goodall et al. (2025). TorchSim: An efficient atomistic simulation engine in PyTorch
27. Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan et al. (2025). Machine learned potential for high-throughput phonon calculations of metal—organic frameworks
28. Matthew K. Horton, Patrick Huck, Ruo Xi Yang et al. (2025). Accelerated data-driven materials science with the Materials Project
29. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). A Foundational Potential Energy Surface Dataset for Materials
30. Matthew C. Kuner, Aaron D. Kaplan, Kristin A. Persson et al. (2025). MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
31. Anyang Peng, Xinzijian Liu, Ming-Yu Guo et al. (2025). The OpenLAM Challenges: LAM Crystal Philately competition
32. Ali Ramlaoui, Martin Siron, Inel Djafar et al. (2025). LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
33. 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
34. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). MatterChat: A Multi-Modal LLM for Material Science
35. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). Siamese Foundation Models for Crystal Structure Prediction
36. K Yan, M Bohde, A Kryvenko (2025). A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
37. M Gibaldi, J Luo, AJ White et al. (2025). Generalizable classification of crystal structure error types using graph attention networks
38. Daniel W. Davies, Keith T. Butler, Adam J. Jackson et al. (2024). SMACT: Semiconducting Materials by Analogy and Chemical Theory
39. Hui Zheng, Eric Sivonxay, Rasmus Christensen et al. (2024). The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity
40. Ilyes Batatia, Philipp Benner, Yuan Chiang et al. (2023). A foundation model for atomistic materials chemistry
41. Jack Douglas Sundberg (2022). A New Framework for Material Informatics and Its Application Toward Electride-Halide Material Systems