Graph deep learning library for materials

The Materials Project Workshop Curriculum

Collection of Python modules & functions to perform and process solid-state defect calculations

Tutorial files for alamode

This repository contains implementations and illustrative code to accompany DeepMind publications

This repository contains a tutorial about GAN's. I have mainly used Vanilla GAN, DCGAN, CDCGAN, and pix2pix models in my tutorials.

The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius subgraphs (i.e., fingerprints) in molecules.

A book on modeling materials using VASP, ase and vasp

Toy example in which electron communicates with python with ZeroMQ

Y-GINI는 논문을 한글로 번역(구글) 해주는 쉬운 도구입니다. 📖

PYFIT-FF is a tool for training feed-forward artificial neural network (NN) interatomic potentials to interpolate between density functional theory energy predictions

A simple stand alone code, designed for comparing structures of two different molecules or clusters.

Slab graph convolutional neural networks for predicting surface-related material properties

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It i…

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Crystal graph convolutional neural networks for predicting material properties.

Unsupervised learning of atomic scale dynamics from molecular dynamics.

A pytorch implemention of GCN-GAN for temporal link prediction.

Study materials about "Deep Learning for Molecular Applications".

Effective Mass Calculator for Semiconductors

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

Calculates various definitions of effective mass from the electronic bandstructure of a semiconductor.

2019 KAIST 딥러닝 홀로서기 세미나용 저장소입니다.

This repository includes weekly reports submitted as in-class assignment of 2018 CH485

CH485 - Artificial Intelligence and Chemistry