Materials Project

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Date: 2011-10-01

Name: Materials Project

Domain: Materials Science

Focus: DFT-based property prediction

Task Types: Property prediction

Metrics: MAE, R^2

Models: Automatminer, Crystal Graph Neural Networks

AI/ML Motif: Regression

Resources

Benchmark: Visit

Datasets: Materials Project Catalysis Explorer

Keywords

DFT materials genome high-throughput

Citation

• Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, and Kristin A. Persson. The materials project: a materials genome approach. APL Materials, 2013. URL: https://materialsproject.org/, doi:10.1063/1.4812323.

@article{jain2013materials,
  title={The Materials Project: A materials genome approach},
  author={Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin A.},
  journal={APL Materials},
  volume    = {1},
  number    = {1},
  year={2013},
  doi       = {10.1063/1.4812323},
  url={https://materialsproject.org/}
}

Ratings

CategoryRating

Software

0.00

No instructions available

Specification

1.50

The platform offers a wide range of material property prediction tasks, but task framing and I/O formats vary by API use and are not always standardized across use cases.

Dataset

3.00

API key required to access data. No predefined splits.

Metrics

5.00

Uses numerical metrics like MAE and $R^2$

Reference Solution

2.00

Numerous models (e.g., Automatminer, CGCNN) trained on the database, but no constraints or documentation listed.

Documentation

0.00

No explanations or paper provided

Average rating: 1.92/5

Radar plot

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