[Dataset request] Graph Network Based Deep Learning of Band Gaps - (VARIOUS) PBE Band Gaps

[gpwolfe]

Name

Gregory Wolfe

Email

[email protected]

Dataset name

Foundry: Graph Network Based Deep Learning of Band Gaps - Materials Project PBE Band Gaps

Authors

Li, Xiang-Guo; Blaiszik, Ben; Schwarting, Marcus; Jacobs, Ryan; Scourtas, Aristana; Schmidt, KJ; Voyles, Paul; Morgan, Dane

Publication link

https://doi.org/10.1063/5.0066009

Data link

doi.org/10.18126/vjwr-5bs9

Additional links

Li, Xiang-Guo; Blaiszik, Ben; Schwarting, Marcus; Jacobs, Ryan; Scourtas, Aristana; Schmidt, KJ; Voyles, Paul; Morgan, Dane

Dataset description

There are 4 different datasets here: one for each of Materials Project, AFLOW, OQMD and experimental results.
Abstract: "Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future. "

File details

json file with pymatgen structure
74,992 configurations

Method

DFT

Method (other)

No response

Software

None

Software (other)

No response

Software version(s)

No response

Additional details

software may be difficult to recover: appears data were gathered from a variety of large databases, inc. MP, OQMD, AFLOW, JARVIS

Property types

Band gap

Other/additional property

No response

Property details

No response

Elements

No response

Number of Configurations

No response

Naming convention

No response

Configuration sets

No response

Configuration labels

No response

Distribution license

No response

Permissions

• I confirm that I have the necessary permissions to submit this dataset