Web apps implementing pre-trained property prediction models.
MatErials Graph Network (MEGNet) models for formation energies, band gaps, elastic constants and other properties of crystals.
Deep neural networks for formation energy prediction of garnets and perovskites.
Random forest models for prediction of coordination environment from X-ray absorption spectra.
Software frameworks for robust AI in materials science.
Library for building graph networks for materials science developed by the Materials Virtual Lab.
Library for machine learning for materials science developed by the Materials Virtual Lab.
Data mining tool for materials science developed by the Hacking Materials group.
The Atomic Energy NETwork (AENet) package is a collection of tools for the construction and application of atomic interaction potentials based on artificial neural networks (ANN) developed by atomistic.net/.
Curated datasets for reproducible AI in materials science.
