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134kmachinelearning

Python machine learning code for the 134k molecules dataset

2020_reactivity_ml

Final model for reactivity prediction

3d-convneuralnet-material-property-prediction

This repo includes the dataset/code of 3D Convolutional Neural Network for material property prediction

alignn

Atomistic Line Graph Neural Network

ann

aware

The code base for AWARE, a graph representation learning method from https://arxiv.org/abs/2110.02667

bestpractices

Things that you should (and should not) do in your Materials Informatics research.

breast_mri_fusion

[CVAMD 2021] "End-to-End Learning of Fused Image and Non-Image Feature for Improved Breast Cancer Classification from MRI"

building-lattices

Building idealized crystal lattices

can_machine_learning_find_extraordinary_materials

Complimentary code for "Can machine learning find extraordinary materials?"

cdvae

An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

cgat

Crystal graph attention neural networks for materials prediction

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chemixnet

Mixed DNN Architectures for Predicting Properties using Multiple Molecular Representations

chemprop

Message Passing Neural Networks for Molecule Property Prediction

cmlkit

tools for machine learning in condensed matter physics and quantum chemistry

combo3

COMBO for Python 3

composition-conditioned-crystal-gan

Composition-Conditioned Crystal GAN pytorch code

computational_statistics

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

crabnet

Predict materials properties using only the composition information!

crabnet-hyperparameter

Using Bayesian optimization via Ax platform + SAASBO model to simultaneously optimize 23 hyperparameters in 100 iterations (set a new Matbench benchmark).

crosspropertytl

crystal-graph-features

Learning physical properties from crystal structure

crystalfeatures

crystalfeatures_tutorial

cspml

Original implementation of CSPML

dalm

deep learning-enabled accurate layer mapping of 2D materials

data-driven-first-principles-methods-for-the-study-and-design-of-alkali-superionic-conductors

Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors"

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.