Perovskite Properties · foundry-ml
perovskite_stability_v1-1
A benchmark dataset of DFT-calculated thermodynamic stability values for 1,929 perovskite oxide compositions.
What perovskite_stability_v1-1 is
This dataset provides density functional theory (DFT) calculated stability and formation energy values for 1,929 perovskite oxide materials. Developed by Wei Li, Ryan Jacobs, and Dane Morgan, it aims to support the training and benchmarking of machine learning models for materials science, chemistry, and energy applications.
Evaluating thermodynamic stability through quantum mechanical simulations is computationally demanding. By pairing perovskite chemical compositions with DFT-derived stability metrics, this dataset enables researchers to build and evaluate predictive models that bypass repeated first-principles calculations.
How it was made
The dataset is arranged in a tabular format comprising chemical compositions alongside calculated target values. Stabilities are quantified as convex hull energies (measured in meV/atom) and formation energies relative to pure elemental references (measured in eV/atom), both determined using density functional theory.
Data records are provided in a single training split, accessible in tabular parquet format for machine learning workflows such as regression and classification.
Key facts
- Total samples
- 1,929 perovskite oxides
- Data format
- Tabular (Parquet)
- Primary input
- Material Composition
- Target outputs
- EnergyAboveHull (meV/atom), Formation_energy (eV/atom)
- Task categories
- Tabular regression, tabular classification
- Authors
- Wei Li, Ryan Jacobs, Dane Morgan
- Year
- 2022
- DOI
- 10.18126/qe5y-2dnz
What you can use it for
- Perovskite Stability Regression
- Train tabular regression models to predict energy above the convex hull directly from chemical composition.
- Formation Energy Estimation
- Develop models to infer DFT formation energy values across candidate oxide materials.
- Thermodynamic Stability Classification
- Classify perovskite compositions as stable or unstable based on convex hull thresholds for screening candidate materials.
Explore the data
The schema, real rows, full-text search and a SQL filter — read in your browser, nothing downloaded.
Explore
Read the real rows without downloading anything
Reading rows…
LiveRead from foundry-ml/perovskite_stability_v1-1 at the moment you asked. Nothing is cached or stored — every row above came from that request.
How the columns are distributed
Over 1,929 rows of default/train
EnergyAboveHull
float- min
- 0
- median
- 84.20
- mean
- 105.53
- max
- 956.83
Formation_energy
float- min
- -3.21
- median
- -1.90
- mean
- -1.91
- max
- -0.49
Material Composition
string_text- min
- 7
- median
- 12
- mean
- 12.53
- max
- 18
Strengths and limits
Good at
- Includes calculated convex hull energy alongside formation energy for consistent thermodynamic evaluation.
- Specifically focuses on 1,929 distinct perovskite oxide compositions.
- Formatted for direct ingestion in tabular machine learning frameworks.
Watch out for
- Contains a single pre-defined train split without designated test or validation partitions.
- Restricted to composition-level input features rather than full atomistic coordinate structures in the primary fields.
Questions
Can I look inside perovskite_stability_v1-1 without downloading it?
Yes. This page reads its schema, real rows and per-column statistics in the browser, with full-text search and a SQL filter. Nothing is downloaded to your machine.
What licence is perovskite_stability_v1-1 under?
Its authors declare cc-by-4.0. Read the terms on the original page before reusing the data.
What target properties are provided in the dataset?
The dataset contains two target properties: EnergyAboveHull in meV/atom and Formation_energy in eV/atom.
How many compositions are covered?
The dataset contains 1,929 perovskite oxide compositions.
What computational method was used to derive the stability values?
The target metrics were computed using density functional theory (DFT).
How to cite it
Li, Wei and Jacobs, Ryan and Morgan, Dane. Predicting the thermodynamic stability of perovskite oxides using machine learning models. Materials Data Facility, 2022. https://doi.org/10.18126/qe5y-2dnz
Written from perovskite_stability_v1-1's own dataset card. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.