Perovskite Properties · foundry-ml
dataset_perovskite_habs
Perovskite hydrogen absorption measurements for machine-learning screening of protonic solid oxide cell materials.
What dataset_perovskite_habs is
Compiled by Ning Wang and colleagues in 2023 and distributed through the Materials Data Facility and Foundry-ML, dataset_perovskite_habs provides 795 tabular records of hydrogen absorption in perovskite oxides. The dataset was assembled to support machine-learning screening of proton-conducting cobalt- and iron-based oxides for the air electrodes of protonic solid oxide cells, which are electrochemical devices used in clean energy conversion.
The collection provides hot-air-balance (HABS) measurements aimed at thermal-stability characterisation to support durability modeling. By recording proton uptake across varying compositions, operational temperatures, and water partial pressures, it enables computational researchers to build predictive regression and classification models for energy materials.
How it was made
The dataset aggregates tabular experimental records from published literature. Each observation details the material composition along with the measurement environment, specifically temperature in degrees Celsius and water partial pressure (pH2O). The primary predicted target is the proton concentration, expressed in moles per formula unit, accompanied by the bibliographic reference for each source study.
Key facts
- Dataset size
- 795 data points
- Modality
- Tabular
- Target property
- Proton concentration (mole/(formula unit))
- Input features
- Composition, temperature_C, pH2O, Reference
- Primary tasks
- Tabular regression, tabular classification
- Publication year
- 2023
- Data provider
- Materials Data Facility / Foundry-ML
What you can use it for
- Predicting proton concentration
- Train regression algorithms to estimate hydrogen absorption in perovskites as a function of composition, temperature, and water partial pressure.
- Screening solid oxide cell electrodes
- Filter cobalt- and iron-based oxides computationally to identify candidate air-electrode materials with favourable proton uptake.
- Thermal stability assessment
- Evaluate the thermal stability and durability of candidate protonic materials across differing thermal and moisture regimes.
Explore the data
The schema, real rows, full-text search and a SQL filter — read in your browser, nothing downloaded.
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Reading rows…
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How the columns are distributed
Over 795 rows of default/train
Composition
string_label- BaZr0.8Y0.2O36%
- BaZr0.9Y0.1O35%
- Ba0.5Sr0.5Co0.8Fe0.2O34%
Reference
string_text- min
- 30
- median
- 35
- mean
- 36.98
- max
- 64
pH2O
float- min
- 5.0e-3
- median
- 0.02
- mean
- 0.02
- max
- 0.20
proton_concentration
float- min
- 0
- median
- 0.02
- mean
- 0.05
- max
- 0.38
temperature_C
int- min
- 50
- median
- 550
- mean
- 590.58
- max
- 4,590
Strengths and limits
Good at
- Focuses specifically on hydrogen absorption and proton concentration in perovskite air-electrode materials.
- Supplies corresponding experimental conditions including measurement temperature and water partial pressure.
- Cites the original experimental reference literature for each entry.
Watch out for
- Contains a relatively small sample size of 795 data points.
- Lacks predefined test and validation splits, supplying only a single train partition.
- Does not specify the measurement unit for water partial pressure (pH2O).
Questions
Can I look inside dataset_perovskite_habs 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_habs under?
Its authors declare other. Read the terms on the original page before reusing the data.
What does this dataset measure?
It measures the proton concentration in perovskite materials, reported in moles per formula unit, under controlled temperature and water partial pressure conditions.
What primary problem does the dataset address?
It provides experimental hydrogen absorption and thermal stability data to train machine-learning models that screen proton-conducting oxides for solid oxide cell air electrodes.
What variables are included as model inputs?
The input variables are material chemical composition, measurement temperature in degrees Celsius, water partial pressure, and the original literature reference.
How to cite it
Wang, Ning, Yuan, Baoyin, Zheng, Fangyuan, Mo, Shanyun, Zhang, Xiaohan, Du, Lei, Xing, Lixin, Meng, Ling, Zhao, Lei, Aoki, Yoshitaka, Tang, Chunmei, Ye, Siyu. Machine-Learning Assisted Screening Proton Conducting Co/Fe based Oxide for the Air Electrode of Protonic Solid Oxide Cell. Materials Data Facility, 2023. https://doi.org/10.18126/zgzt-xr34
Written from dataset_perovskite_habs'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.