Active learning of GNN partial charge predictors for MOFs

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

Extended DeepH (xDeepH) method for magnetic materials.

Deep neural networks for density functional theory Hamiltonian.

C++ Programming Tutorial in Chemistry

告别枯燥，致力于打造 Python 实用小例子，更多Python良心教程见 https://ai-jupyter.com

scripts related to qe

Code and data for 'From bulk effective mass to two-dimensional carrier mobility —— accurate prediction from adversarial transfer learning'

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

Some useful vasp scripts and tools

Use of Riemannian manifolds instead of real vector spaces for deep learning in NLP.

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

时间序列分析教程

用机器学习的方法预测二维材料的物理性质

📒《统计学习方法-李航: 笔记-从原理到实现，基于R语言》200页PDF，各种手推公式细节讲解，R语言实现. 🎉🎉

【2024年新版】国科大 陈云霁 智能计算系统AICS实验代码

AICS: 智能计算系统

计算机自学指南

Must-read papers on graph neural networks (GNN)

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

Graph Convolutional Networks in PyTorch

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

GNN综述阅读报告

Graph Attention Networks (https://arxiv.org/abs/1710.10903)