Perovskite Properties · foundry-ml
dataset_perovskite_conductivity
Perovskite conductivity measurements supporting machine learning screening for solid-oxide fuel cell electrolytes.
What dataset_perovskite_conductivity is
dataset_perovskite_conductivity is a tabular scientific dataset developed by Pikee Priya and N. R. Aluru, published via the Materials Data Facility and Foundry-ML. It brings together 7,230 experimental data points documenting the electrical transport properties of perovskite materials, including ionic and electronic conductivity measurements.
The dataset addresses the challenge of identifying high-conductivity perovskite compositions for energy applications, specifically solid-oxide fuel cell electrolytes. By compiling standardised measurements alongside synthesis and operational conditions, it enables computational researchers to build predictive regression and classification models rather than relying entirely on manual, trial-and-error synthesis.
How it was made
The dataset compiles tabular records sourced from scientific literature, referencing each original publication. Each record pairs environmental and structural variables—such as measurement temperature, oxygen partial pressure, water partial pressure, and grain size—with the material's composition and dominant charge carrier type.
Total conductivity, measured in Siemens per centimetre (S/cm), serves as the primary target variable. The collection is organized into a single training split formatted as tabular data, available in parquet and text-compatible structures.
Key facts
- Authors
- Priya, Pikee; Aluru, N. R.
- Release year
- 2021
- DOI
- 10.18126/f1vb-et73
- Record count
- 7,230 data points
- Format
- Tabular (Parquet)
- Target variable
- Conductivity (S/cm)
- Primary tasks
- Tabular regression, tabular classification
What you can use it for
- Fuel cell electrolyte screening
- Train machine learning models to identify high-conductivity perovskite compositions for solid-oxide fuel cell electrolytes.
- Conductivity regression modelling
- Predict total conductivity in S/cm across varied temperatures, partial pressures, and grain sizes.
- Charge carrier classification
- Classify and analyse dominant carrier types based on material composition and environmental operating conditions.
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…
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How the columns are distributed
Over 7,230 rows of default/train
Carrier Type
string_label- H30%
- O+E23%
- E20%
Composition
string_label- (La2O3)0.35(SrO)0.3(Cr2O3)0.25(Mn2O3)0.253%
- (La2O3)0.375(SrO)0.25(Cr2O3)0.25(Mn2O3)0.253%
- (La2O3)0.4(SrO)0.2(Cr2O3)0.25(Mn2O3)0.252%
Conductivity (S/cm)
float- min
- 0
- median
- 0.03
- mean
- 43.09
- max
- 7,100
Grainsize
float- min
- 8.0e-3
- median
- 1.90
- mean
- 7.39
- max
- 356.74
84.0% empty
PH2O (%)
float- min
- 0
- median
- 0
- mean
- 0.91
- max
- 10
PO2 (%)
float- min
- 0
- median
- 1.1e-3
- mean
- 14.22
- max
- 2230.25
Ref
string_label- https://doi.org/10.1016/j.ssi.2006.02.0255%
- https://doi.org/10.1016/j.ssi.2013.01.0054%
- https://doi.org/10.1039/A702865B3%
Temperature (degC)
float- min
- 20.98
- median
- 667.59
- mean
- 649.43
- max
- 1421.52
Strengths and limits
Good at
- Provides 7,230 curated data points combining environmental and structural conditions alongside conductivity values.
- Includes essential operational variables such as oxygen partial pressure, water partial pressure, and temperature.
- Retains links to the original literature references for each experimental record.
Watch out for
- Does not include predefined validation or test splits, providing only a train split.
- Restricted to tabular measurements without crystallographic structural coordinate files (such as CIFs).
Questions
Can I look inside dataset_perovskite_conductivity 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 dataset_perovskite_conductivity under?
Its authors declare other. Read the terms on the original page before reusing the data.
What is the primary target variable in this dataset?
The target property is total conductivity, recorded in Siemens per centimetre (S/cm).
What input features are recorded for each material?
The inputs include material composition, measurement temperature in degrees Celsius, oxygen partial pressure, water partial pressure, grain size in microns, carrier type, and the original literature reference.
How many data points are included?
The dataset contains 7,230 perovskite conductivity measurements.
How to cite it
Priya, Pikee and Aluru, N. R. Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning. Materials Data Facility, 2021. doi:10.18126/f1vb-et73.
Written from dataset_perovskite_conductivity'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.