https://github.com/janosh/pymatviz
A toolkit for visualizations in materials informatics.
https://github.com/janosh/pymatviz
data-visualization machine-learning materials-informatics materials-science matplotlib plotly plots python uncertainty uncertainty-calibration
Last synced: 8 days ago
JSON representation
A toolkit for visualizations in materials informatics.
- Host: GitHub
- URL: https://github.com/janosh/pymatviz
- Owner: janosh
- License: mit
- Created: 2021-02-21T12:40:34.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-18T20:34:09.000Z (6 months ago)
- Last Synced: 2024-10-20T06:11:34.124Z (6 months ago)
- Topics: data-visualization, machine-learning, materials-informatics, materials-science, matplotlib, plotly, plots, python, uncertainty, uncertainty-calibration
- Language: Python
- Homepage: https://janosh.github.io/pymatviz
- Size: 72.8 MB
- Stars: 164
- Watchers: 7
- Forks: 15
- Open Issues: 16
-
Metadata Files:
- Readme: readme.md
- Changelog: changelog.md
- License: license
- Citation: citation.cff
Awesome Lists containing this project
- awesome-materials-informatics - Pymatviz - A toolkit for visualizations in materials informatics. [](https://github.com/janosh/pymatviz) (Software and products)
- best-of-atomistic-machine-learning - GitHub - 20% open · ⏱️ 08.04.2025): (Visualization)
README
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)
[fig-icon]: https://api.iconify.design/lsicon:scatter-diagram-outline.svg?color=%234c8bf5&height=16 "View example code"
> If you use `pymatviz` in your research, [see how to cite](#how-to-cite-pymatviz). Check out [23 existing papers using `pymatviz`](#papers-using-pymatviz) for inspiration!
## Installation
```sh
pip install pymatviz
```
Available extras include:
```sh
pip install 'pymatviz[pdf-export]' # save figures to PDF
pip install 'pymatviz[brillouin]' # render 3d Brillouin zones
```
## API Docs
See the [/api][/api] page.
[/api]: https://janosh.github.io/pymatviz/api
## Usage
See the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! 🙏
| | | |
| ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
| [matbench_dielectric_eda.ipynb](examples/matbench_dielectric_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [Launch Codespace][codespace url] |
| [mp_bimodal_e_form.ipynb](examples/mp_bimodal_e_form.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [Launch Codespace][codespace url] |
| [matbench_perovskites_eda.ipynb](examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [Launch Codespace][codespace url] |
| [mprester_ptable.ipynb](examples/mprester_ptable.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [Launch Codespace][codespace url] |
[Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg
[codespace url]: https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=340898532
## Periodic Table
See [`pymatviz/ptable/ptable_plotly.py`](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](https://plotly.com) 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)`](pymatviz/ptable/ptable_plotly.py#L38) | [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable/ptable_plotly.py#L38) [![fig-icon]](assets/scripts/ptable_plotly/ptable_heatmap_plotly.py) |
| :------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![ptable-heatmap-plotly-more-hover-data] | ![ptable-heatmap-plotly-log] |
| [`ptable_hists_plotly(data)`](pymatviz/ptable/ptable_plotly.py#L398) [![fig-icon]](assets/scripts/ptable_plotly/ptable_hists_plotly.py) | [`ptable_scatter_plotly(data, mode="markers")`](pymatviz/ptable/ptable_plotly.py#L1359) [![fig-icon]](assets/scripts/ptable_plotly/ptable_scatter_plotly.py) |
| ![ptable-hists-plotly] | ![ptable-scatter-plotly-markers] |
| [`ptable_heatmap_splits_plotly(2_vals_per_elem)`](pymatviz/ptable/ptable_plotly.py#L778) [![fig-icon]](assets/scripts/ptable_plotly/ptable_heatmap_splits_plotly.py) | [`ptable_heatmap_splits_plotly(3_vals_per_elem)`](pymatviz/ptable/ptable_plotly.py#L778) |
| ![ptable-heatmap-splits-plotly-2] | ![ptable-heatmap-splits-plotly-3] |
[ptable-heatmap-plotly-log]: assets/svg/ptable-heatmap-plotly-log.svg
[ptable-heatmap-plotly-more-hover-data]: assets/svg/ptable-heatmap-plotly-more-hover-data.svg
[ptable-heatmap-splits-plotly-2]: assets/svg/ptable-heatmap-splits-plotly-2.svg
[ptable-heatmap-splits-plotly-3]: assets/svg/ptable-heatmap-splits-plotly-3.svg
[ptable-hists-plotly]: assets/svg/ptable-hists-plotly.svg
[ptable-scatter-plotly-markers]: assets/svg/ptable-scatter-plotly-markers.svg
### Dash app using `ptable_heatmap_plotly()`
See [`examples/mprester_ptable.ipynb`](examples/mprester_ptable.ipynb).
[https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4](https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4)
## Phonons
| [`phonon_bands(bands_dict)`](pymatviz/phonons/plotly.py#L35) [![fig-icon]](assets/scripts/phonons/phonon_bands.py) | [`phonon_dos(doses_dict)`](pymatviz/phonons/plotly.py#L381) [![fig-icon]](assets/scripts/phonons/phonon_dos.py) |
| :----------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| ![phonon-bands] | ![phonon-dos] |
| [`phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons/plotly.py#L528) | [`phonon_bands_and_dos(single_bands, single_dos)`](pymatviz/phonons/plotly.py#L528) [![fig-icon]](assets/scripts/phonons/phonon_bands_and_dos.py) |
| ![phonon-bands-and-dos-mp-2758] | ![phonon-bands-and-dos-mp-23907] |
[phonon-bands]: assets/svg/phonon-bands-mp-2758.svg
[phonon-dos]: assets/svg/phonon-dos-mp-2758.svg
[phonon-bands-and-dos-mp-2758]: assets/svg/phonon-bands-and-dos-mp-2758.svg
[phonon-bands-and-dos-mp-23907]: assets/svg/phonon-bands-and-dos-mp-23907.svg
### Composition Clustering
| [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=2)`](pymatviz/cluster/composition/plot.py#L32) [![fig-icon]](assets/scripts/cluster/composition/cluster_compositions_matbench.py) | [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=3)`](pymatviz/cluster/composition/plot.py#L32) |
| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| ![matbench-perovskites-magpie-pca-2d] | ![matbench-perovskites-magpie-tsne-3d] |
[matbench-perovskites-magpie-pca-2d]: assets/svg/matbench-perovskites-magpie-pca-2d.svg
[matbench-perovskites-magpie-tsne-3d]: assets/svg/matbench-perovskites-magpie-tsne-3d.svg
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.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`](pymatviz/structure_viz/plotly.py).
| [`structure_3d_plotly(hea_structure)`](pymatviz/structure_viz/plotly.py#L318) | [`structure_3d_plotly(lco_supercell)`](pymatviz/structure_viz/plotly.py#L318) [![fig-icon]](assets/scripts/structure_viz/structure_3d_plotly.py) |
| :-------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------: |
| ![hea-structure-3d-plotly] | ![lco-structure-3d-plotly] |
| [`structure_2d_plotly(six_structs)`](pymatviz/structure_viz/plotly.py#L42) [![fig-icon]](assets/scripts/structure_viz/structure_2d_plotly.py) | [`structure_3d_plotly(six_structs)`](pymatviz/structure_viz/plotly.py#L318) [![fig-icon]](assets/scripts/structure_viz/structure_3d_plotly.py) |
| ![matbench-phonons-structures-2d-plotly] | ![matbench-phonons-structures-3d-plotly] |
[matbench-phonons-structures-2d-plotly]: assets/svg/matbench-phonons-structures-2d-plotly.svg
[matbench-phonons-structures-3d-plotly]: assets/svg/matbench-phonons-structures-3d-plotly.svg
[hea-structure-3d-plotly]: assets/svg/hea-structure-3d-plotly.svg
[lco-structure-3d-plotly]: assets/svg/lco-structure-3d-plotly.svg
## Brillouin Zone
See [`pymatviz/brillouin.py`](pymatviz/brillouin.py).
| [`brillouin_zone_3d(cubic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(hexagonal_struct)`](pymatviz/brillouin.py#L15) |
| :------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------: |
| ![brillouin-cubic-mp-10018] | ![brillouin-hexagonal-mp-862690] |
| [`brillouin_zone_3d(monoclinic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(orthorhombic_struct)`](pymatviz/brillouin.py#L15) |
| ![brillouin-monoclinic-mp-1183089] | ![brillouin-volumes-3-cols] |
[brillouin-cubic-mp-10018]: assets/svg/brillouin-cubic-mp-10018.svg
[brillouin-hexagonal-mp-862690]: assets/svg/brillouin-hexagonal-mp-862690.svg
[brillouin-monoclinic-mp-1183089]: assets/svg/brillouin-monoclinic-mp-1183089.svg
[brillouin-orthorhombic-mp-1183085]: assets/svg/brillouin-orthorhombic-mp-1183085.svg
[brillouin-volumes-3-cols]: assets/svg/brillouin-volumes-3-cols.svg
## X-Ray Diffraction
See [`pymatviz/xrd.py`](pymatviz/xrd.py).
| [`xrd_pattern(pattern)`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern({key1: patt1, key2: patt2})`](pymatviz/xrd.py#L42) |
| :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------: |
| ![xrd-pattern] | ![xrd-pattern-multiple] |
| [`xrd_pattern(struct_dict, stack="horizontal")`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern(struct_dict, stack="vertical")`](pymatviz/xrd.py#L42) |
| ![xrd-pattern-horizontal-stack] | ![xrd-pattern-vertical-stack] |
[xrd-pattern]: assets/svg/xrd-pattern.svg
[xrd-pattern-multiple]: assets/svg/xrd-pattern-multiple.svg
[xrd-pattern-horizontal-stack]: assets/svg/xrd-pattern-horizontal-stack.svg
[xrd-pattern-vertical-stack]: assets/svg/xrd-pattern-vertical-stack.svg
## Radial Distribution Functions
See [`pymatviz/rdf/plotly.py`](pymatviz/rdf/plotly.py).
| [`element_pair_rdfs(pmg_struct)`](pymatviz/rdf/plotly.py#L33) | [`element_pair_rdfs({"A": struct1, "B": struct2})`](pymatviz/rdf/plotly.py#L33) [![fig-icon]](assets/scripts/rdf/element_pair_rdfs.py) |
| :-----------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------: |
| ![element-pair-rdfs-Na8Nb8O24] | ![element-pair-rdfs-crystal-vs-amorphous] |
[element-pair-rdfs-Na8Nb8O24]: assets/svg/element-pair-rdfs-Na8Nb8O24.svg
[element-pair-rdfs-crystal-vs-amorphous]: assets/svg/element-pair-rdfs-crystal-vs-amorphous.svg
## Coordination
See [`pymatviz/coordination/plotly.py`](pymatviz/coordination/plotly.py).
| [`coordination_hist(struct_dict)`](pymatviz/coordination/plotly.py#L34) | [`coordination_hist(struct_dict, by_element=True)`](pymatviz/coordination/plotly.py#L34) [![fig-icon]](assets/scripts/coordination/coordination_hist.py) |
| :-----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![coordination-hist-single] | ![coordination-hist-by-structure-and-element] |
| [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) | [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) [![fig-icon]](assets/scripts/coordination/coordination_vs_cutoff_line.py#L52) |
| ![coordination-vs-cutoff-single] | ![coordination-vs-cutoff-multiple] |
[coordination-hist-single]: assets/svg/coordination-hist-single.svg
[coordination-hist-by-structure-and-element]: assets/svg/coordination-hist-by-structure-and-element.svg
[coordination-vs-cutoff-single]: assets/svg/coordination-vs-cutoff-single.svg
[coordination-vs-cutoff-multiple]: assets/svg/coordination-vs-cutoff-multiple.svg
## Sunburst
See [`pymatviz/sunburst.py`](pymatviz/sunburst.py).
| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py#L111) [![fig-icon]](assets/scripts/sunburst/spacegroup_sunburst.py) | [`chem_sys_sunburst(["FeO", "Fe2O3", "LiPO4", ...])`](pymatviz/sunburst.py#L206) [![fig-icon]](assets/scripts/sunburst/chem_sys_sunburst.py) |
| :-----------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------: |
| ![spg-num-sunburst] | ![chem-sys-sunburst-ward-bmg] |
[spg-num-sunburst]: assets/svg/spg-num-sunburst.svg
[chem-sys-sunburst-ward-bmg]: assets/svg/chem-sys-sunburst-ward-bmg.svg
## Treemap
See [`pymatviz/treemap.py`](pymatviz/treemap.py).
| [`chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...])`](pymatviz/treemap.py#L36) [![fig-icon]](assets/scripts/treemap/chem_sys_treemap.py) | [`chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...], group_by="formula")`](pymatviz/treemap.py#L36) |
| :-------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
| ![chem-sys-treemap-formula] | ![chem-sys-treemap-ward-bmg] |
[chem-sys-treemap-formula]: assets/svg/chem-sys-treemap-formula.svg
[chem-sys-treemap-ward-bmg]: assets/svg/chem-sys-treemap-ward-bmg.svg
## Rainclouds
See [`pymatviz/rainclouds.py`](pymatviz/rainclouds.py).
| [`rainclouds(two_key_dict)`](pymatviz/rainclouds.py#L20) [![fig-icon]](assets/scripts/rainclouds/rainclouds.py) | [`rainclouds(three_key_dict)`](pymatviz/rainclouds.py#L20) |
| :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------: |
| ![rainclouds-bimodal] | ![rainclouds-trimodal] |
[rainclouds-bimodal]: assets/svg/rainclouds-bimodal.svg
[rainclouds-trimodal]: assets/svg/rainclouds-trimodal.svg
## Sankey
See [`pymatviz/sankey.py`](pymatviz/sankey.py).
| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py#L16) [![fig-icon]](assets/scripts/sankey/sankey_from_2_df_cols.py) | [`sankey_from_2_df_cols(df_space_groups)`](pymatviz/sankey.py#L16) |
| :-----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------: |
| ![sankey-spglib-vs-aflow-spacegroups] | ![sankey-crystal-sys-to-spg-symbol] |
[sankey-spglib-vs-aflow-spacegroups]: assets/svg/sankey-spglib-vs-aflow-spacegroups.svg
[sankey-crystal-sys-to-spg-symbol]: assets/svg/sankey-crystal-sys-to-spg-symbol.svg
## Bar Plots
See [`pymatviz/bar.py`](pymatviz/bar.py).
| [`spacegroup_bar([65, 134, 225, ...], backend="plotly")`](pymatviz/bar.py#L29) [![fig-icon]](assets/scripts/bar/spacegroup_bar.py) | [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")`](pymatviz/bar.py#L29) |
| :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------: |
| ![spg-num-hist-plotly] | ![spg-symbol-hist-plotly] |
[spg-symbol-hist-plotly]: assets/svg/spg-symbol-hist-plotly.svg
[spg-num-hist-plotly]: assets/svg/spg-num-hist-plotly.svg
## Histograms
See [`pymatviz/histogram.py`](pymatviz/histogram.py).
| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/histogram.py#L37) [![fig-icon]](assets/scripts/histogram/elements_hist.py) | [`histogram({'key1': values1, 'key2': values2})`](pymatviz/histogram.py#L108) [![fig-icon]](assets/scripts/histogram/histogram.py) |
| :-----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
| ![elements-hist] | ![histogram-ecdf] |
[histogram-ecdf]: assets/svg/histogram-ecdf.svg
## Scatter Plots
See [`pymatviz/scatter.py`](pymatviz/scatter.py).
| [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) | [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) [![fig-icon]](assets/scripts/scatter/density_scatter_plotly.py) |
| :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| ![density-scatter-plotly] | ![density-scatter-plotly-blobs] |
| [`density_scatter(xs, ys, ...)`](pymatviz/scatter.py#L71) [![fig-icon]](assets/scripts/scatter/density_scatter.py) | [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L570) [![fig-icon]](assets/scripts/scatter/density_scatter_with_hist.py) |
| ![density-scatter] | ![density-scatter-with-hist] |
| [`density_hexbin(xs, ys, ...)`](pymatviz/scatter.py#L493) [![fig-icon]](assets/scripts/scatter/density_hexbin.py) | [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L587) [![fig-icon]](assets/scripts/scatter/density_hexbin_with_hist.py) |
| ![density-hexbin] | ![density-hexbin-with-hist] |
[density-scatter-plotly]: assets/svg/density-scatter-plotly.svg
[density-scatter-plotly-blobs]: assets/svg/density-scatter-plotly-blobs.svg
[density-hexbin-with-hist]: assets/svg/density-hexbin-with-hist.svg
[density-hexbin]: assets/svg/density-hexbin.svg
[density-scatter-with-hist]: assets/svg/density-scatter-with-hist.svg
[density-scatter]: assets/svg/density-scatter.svg
## Uncertainty
See [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).
| [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L27) [![fig-icon]](assets/scripts/uncertainty/qq_gaussian.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L27) |
| :---------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |
| ![normal-prob-plot] | ![normal-prob-plot-multiple] |
| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L208) [![fig-icon]](assets/scripts/uncertainty/error_decay_with_uncert.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L208) |
| ![error-decay-with-uncert] | ![error-decay-with-uncert-multiple] |
## Classification
See [`pymatviz/classify/confusion_matrix.py`](pymatviz/classify/confusion_matrix.py).
| [`confusion_matrix(conf_mat, ...)`](pymatviz/classify/confusion_matrix.py#L10) | [`confusion_matrix(y_true, y_pred, ...)`](pymatviz/classify/confusion_matrix.py#L10) [![fig-icon]](assets/scripts/classify/confusion_matrix.py) |
| :----------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| ![stability-confusion-matrix] | ![crystal-system-confusion-matrix] |
See [`pymatviz/classify/curves.py`](pymatviz/classify/curves.py).
| [`roc_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L76) [![fig-icon]](assets/scripts/classify/roc_curve.py) | [`precision_recall_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L176) [![fig-icon]](assets/scripts/classify/precision_recall_curve.py) |
| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![roc-curve-plotly-multiple] | ![precision-recall-curve-plotly-multiple] |
[roc-curve-plotly-multiple]: assets/svg/roc-curve-plotly-multiple.svg
[precision-recall-curve-plotly-multiple]: assets/svg/precision-recall-curve-plotly-multiple.svg
[stability-confusion-matrix]: assets/svg/stability-confusion-matrix.svg
[crystal-system-confusion-matrix]: assets/svg/crystal-system-confusion-matrix.svg
[error-decay-with-uncert-multiple]: assets/svg/error-decay-with-uncert-multiple.svg
[error-decay-with-uncert]: assets/svg/error-decay-with-uncert.svg
[elements-hist]: assets/svg/elements-hist.svg
[normal-prob-plot-multiple]: assets/svg/normal-prob-plot-multiple.svg
[normal-prob-plot]: assets/svg/normal-prob-plot.svg
[struct-2d-mp-12712-Hf9Zr9Pd24-disordered]: assets/svg/struct-2d-mp-12712-Hf9Zr9Pd24-disordered.svg
[struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]: assets/svg/struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered.svg
## How to cite `pymatviz`
See [`citation.cff`](citation.cff) or cite the [Zenodo record](https://zenodo.org/badge/latestdoi/340898532) 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 2025-03-25. Auto-generated [from Google Scholar](https://scholar.google.com/scholar?q=pymatviz). Manual additions [via PR](https://github.com/janosh/pymatviz/edit/main/readme.md) welcome.
1. C Zeni, R Pinsler, D Zügner et al. (2023). [Mattergen: a generative model for inorganic materials design](https://arxiv.org/abs/2312.03687) (cited by 123)
1. J Riebesell, REA Goodall, P Benner et al. (2023). [Matbench Discovery--A framework to evaluate machine learning crystal stability predictions](https://arxiv.org/abs/2308.14920) (cited by 45)
1. 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](https://pubs.acs.org/doi/abs/10.1021/jacs.4c03849) (cited by 36)
1. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). [Open materials 2024 (omat24) inorganic materials dataset and models](https://www.rivista.ai/wp-content/uploads/2024/10/2410.12771v1.pdf) (cited by 31)
1. M Giantomassi, G Materzanini (2024). [Systematic assessment of various universal machine‐learning interatomic potentials](https://onlinelibrary.wiley.com/doi/abs/10.1002/mgea.58) (cited by 15)
1. AA Naik, C Ertural, P Benner et al. (2023). [A quantum-chemical bonding database for solid-state materials](https://www.nature.com/articles/s41597-023-02477-5) (cited by 12)
1. K Li, AN Rubungo, X Lei et al. (2025). [Probing out-of-distribution generalization in machine learning for materials](https://www.nature.com/articles/s43246-024-00731-w) (cited by 8)
1. N Tuchinda, CA Schuh (2025). [Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles](https://arxiv.org/abs/2502.01579) (cited by 2)
1. A Onwuli, KT Butler, A Walsh (2024). [Ionic species representations for materials informatics](https://pubs.aip.org/aip/aml/article/2/3/036112/3313198) (cited by 2)
1. A Kapeliukha, RA Mayo (2025). [MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations](https://pubs.rsc.org/en/content/articlehtml/2025/sc/d4sc07438f) (cited by 1)
1. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). [A Foundational Potential Energy Surface Dataset for Materials](http://arxiv.org/abs/2503.04070)
1. 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](http://arxiv.org/abs/2502.03578)
1. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). [MatterChat: A Multi-Modal LLM for Material Science](http://arxiv.org/abs/2502.13107)
1. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). [Siamese Foundation Models for Crystal Structure Prediction](http://arxiv.org/abs/2503.10471)
1. A Peng, MY Guo (2025). [The OpenLAM Challenges](https://arxiv.org/abs/2501.16358)
1. F Therrien, JA Haibeh (2025). [OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes](https://arxiv.org/abs/2502.14234)
1. N Tuchinda, CA Schuh (2025). [A Grain Boundary Embrittlement Genome for Substitutional Cubic Alloys](https://arxiv.org/abs/2502.06531)
1. K Yan, M Bohde, A Kryvenko (2025). [A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures](https://arxiv.org/abs/2503.05771)
1. Daniel W. Davies, Keith T. Butler, Adam J. Jackson et al. (2024). [SMACT: Semiconducting Materials by Analogy and Chemical Theory](https://github.com/WMD-group/SMACT)
1. Hui Zheng, Eric Sivonxay, Rasmus Christensen et al. (2024). [The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity](https://www.nature.com/articles/s41524-024-01469-2)
1. HH Li, Q Chen, G Ceder (2024). [Voltage Mining for (De) lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage](https://pubs.acs.org/doi/abs/10.1021/acsami.4c15742)
1. Janosh Riebesell, Ilyes Batatia, Philipp Benner et al. (2023). [A foundation model for atomistic materials chemistry](https://arxiv.org/abs/2401.00096v1)
1. Jack Douglas Sundberg (2022). [A New Framework for Material Informatics and Its Application Toward Electride-Halide Material Systems](https://cdr.lib.unc.edu/concern/dissertations/r494vw405)