FrancisBlackLee
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State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
An open-source Python package for creating fast and accurate interatomic potentials.
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
SchNetPack - Deep Neural Networks for Atomistic Systems
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
AI education materials for Chinese students, teachers and IT professionals.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
12 Weeks, 24 Lessons, AI for All!
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
A deep learning package for many-body potential energy representation and molecular dynamics
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Graph deep learning library for materials
Deep neural networks for density functional theory Hamiltonian.
The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field
Interatomic potential creating using DFT training data.
DScribe is a python package for creating machine learning descriptors for atomistic systems.
Simple examples to introduce PyTorch
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Official Repository for the Uni-Mol Series Methods
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.