11 datasets found

The SuperCon database contains information about superconductors, including their critical temperatures, and is used to train machine learning models to predict the critical...

A dataset of interatomic potentials for training machine learning models.

The dataset used to train the crystal graph convolutional neural networks (CGCNN) model to visualize the compositional and structural similarities between materials in an...

A dataset of catalytic materials for training machine learning models.

A broad benchmark for applying machine learning models to solid-state materials.

A dataset of 10,790 predicted perovskites, along with their 5 most similar experimental materials, used to test the effectiveness of the VAE model.

A dataset of 2104 experimental materials, including their crystal structure information, used to train a Variational Autoencoder (VAE) model.

The dataset used for machine learning accelerated discovery of porous organic cages.

The dataset used for machine learning accelerated discovery of metal-organic frameworks.

The dataset used for machine learning accelerated discovery of transition-metal complexes.

The Open Catalyst 2020 (OC20) dataset is a large-scale dataset for simulating catalyst materials.