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cgcnn

Crystal graph convolutional neural networks for predicting material properties.

darwin

Interpretable discovery of materials using machine learning

e3nn

A modular framework for neural networks with Euclidean symmetry

egnn

E(n) Equivariant Graph Neural Networks

flatband_ptr

using PTR model for the flat band prediction

ggnn4science

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

interpretable_ai_for_battery

Interpretable learning for electrode-voltage prediction and design of multivalent metal-ion batteries

ipcsp

Integer Programming encoding for Crystal Structure Prediction with classic and quantum computing bindings

m3gnet

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.

pgcgm

Source code for generating materials with 20 space groups using PGCGM

twod_structures

a series of python scripts to standardize 2D structures or other surface models

voltages-of-battery-electrodes

Using the CGCNN transfer learning model to pridict the voltages of many kinds of metal-ion battery electrodes