---
license: bsd-3-clause
task_categories:
- graph-ml
language:
- en
tags:
- chemistry
- materials
size_categories:
- 100K<n<1M
dataset_info:
  config_name: default
  features:
  # ---- Composition / chemistry ----
  - name: nsites
    dtype: int32
  - name: elements
    sequence: string
  - name: nelements
    dtype: int32
  - name: composition
    sequence:
    - name: element
      dtype: string
    - name: amount
      dtype: float64
  - name: composition_reduced
    sequence:
    - name: element
      dtype: string
    - name: amount
      dtype: float64
  - name: formula_pretty
    dtype: string
  - name: formula_anonymous
    dtype: string
  - name: chemsys
    dtype: string

  # ---- Cell-level scalars ----
  - name: volume
    dtype: float64
  - name: density
    dtype: float64
  - name: density_atomic
    dtype: float64

  # ---- Symmetry ----
  - name: symmetry
    struct:
    - name: crystal_system
      dtype: string
    - name: symbol
      dtype: string
    - name: number
      dtype: int32
    - name: point_group
      dtype: string
    - name: symprec
      dtype: float64
    - name: angle_tolerance
      dtype: float64
    - name: version
      dtype: string

  # ---- Pymatgen Structure ----
  - name: structure
    struct:
    - name: '@module'
      dtype: string
    - name: '@class'
      dtype: string
    - name: charge
      dtype: float64
    - name: lattice
      struct:
      - name: matrix
        sequence:
          sequence: float64
      - name: pbc
        sequence: bool
      - name: a
        dtype: float64
      - name: b
        dtype: float64
      - name: c
        dtype: float64
      - name: alpha
        dtype: float64
      - name: beta
        dtype: float64
      - name: gamma
        dtype: float64
      - name: volume
        dtype: float64
    - name: properties
      dtype: string
    - name: sites
      sequence:
      - name: species
        sequence:
        - name: element
          dtype: string
        - name: occu
          dtype: float64
      - name: abc
        sequence: float64
      - name: properties
        struct:
        - name: magmom
          dtype: float64
      - name: label
        dtype: string
      - name: xyz
        sequence: float64
  
  # ---- Labels (DFT targets) ----
  - name: energy
    dtype: float64
  - name: forces
    sequence:
      sequence: float64
  - name: stress
    sequence: float64

  # ---- Identifiers / derived properties ----
  - name: matpes_id
    dtype: string
  - name: bandgap
    dtype: float64
  - name: functional
    dtype: string
  - name: formation_energy_per_atom
    dtype: float64
  - name: cohesive_energy_per_atom
    dtype: float64
  - name: abs_forces
    sequence: float64
  - name: bader_charges
    sequence: float64
  - name: bader_magmoms
    sequence: float64

  # ---- Provenance (MD sampling + MP origin) ----
  - name: provenance
    struct:
    - name: original_mp_id
      dtype: string
    - name: materials_project_version
      dtype: string
    - name: md_ensemble
      dtype: string
    - name: md_temperature
      dtype: float64
    - name: md_pressure
      dtype: float64
    - name: md_step
      dtype: int32
    - name: mlip_name
      dtype: string

configs:
  - config_name: pbe
    data_files: 
    - split: train
      path: MatPES-PBE-2025.2.json
  - config_name: r2scan
    data_files: 
    - split: train
      path: MatPES-R2SCAN-2025.2.json
  - config_name: pbe-2025.2
    data_files: MatPES-PBE-2025.2.json
  - config_name: r2scan-2025.2
    data_files: MatPES-R2SCAN-2025.2.json
  - config_name: pbe-2025.1
    data_files: MatPES-PBE-2025.1.json
  - config_name: r2scan-2025.1
    data_files: MatPES-R2SCAN-2025.1.json
  - config_name: pbe-atoms
    data_files: MatPES-PBE-atoms.json
  - config_name: r2scan-atoms
    data_files: MatPES-R2SCAN-atoms.json
papers:
  - 2503.04070
---

## Dataset Description

- **Homepage:** [matpes.ai](http://matpes.ai)
- **Paper:** [A Foundational Potential Energy Surface Dataset for Materials](https://doi.org/10.48550/arXiv.2503.04070)
- **Leaderboard:** [MatCalc-Benchmark](http://matpes.ai/benchmarks)
- **Point of Contact:** [Materialyze]

### Dataset Summary

Potential energy surface datasets with near-complete coverage of the periodic table are used to train foundation
potentials (FPs), i.e., machine learning interatomic potentials (MLIPs) with near-complete coverage of the periodic
table.  MatPES is an initiative by the [Materialyze] Lab and the [Materials Project] to address
[critical deficiencies](http://matpes.ai/about) in such PES datasets for materials.

1. **Accuracy.** MatPES is computed using static DFT calculations with stringent converegence criteria.
   Please refer to the `MatPESStaticSet` in [pymatgen] for details.
2. **Comprehensiveness.** MatPES structures are sampled using a 2-stage version of DImensionality-Reduced
   Encoded Clusters with sTratified [DIRECT](https//doi.org/10.1038/s41524-024-01227-4) sampling from a greatly expanded configuration of MD structures.
3. **Quality.** MatPES includes computed data from the PBE functional, as well as the high fidelity r2SCAN meta-GGA
   functional with improved description across diverse bonding and chemistries.

The initial v2025.1 release comprises ~400,000 structures from 300K MD simulations. This dataset is much smaller
than other PES datasets in the literature and yet achieves comparable or, in some cases,
[improved performance and reliability](http://matpes.ai/benchmarks) on trained FPs.

MatPES is part of the MatML ecosystem, which includes the [MatGL] (Materials Graph Library) and [maml] (MAterials
Machine Learning) packages, the [MatPES] (Materials Potential Energy Surface) dataset, and the [MatCalc] (Materials
Calculator).


[Materialyze]: http://materialyze.ai
[Materials Project]: https://materialsproject.org
[M3GNet]: http://dx.doi.org/10.1038/s43588-022-00349-3
[CHGNet]: http://doi.org/10.1038/s42256-023-00716-3
[TensorNet]: https://arxiv.org/abs/2306.06482
[maml]: https://materialsvirtuallab.github.io/maml/
[MatGL]: https://matgl.ai
[MatPES]: https://matpes.ai
[MatCalc]: https://matcalc.ai