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cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural n…
Pytorch implementation of set transformer
A high-performance toolkit for quantum and classical chemistry calculations.
This repo is for the Linkedin Learning course: Level up: C++
This repo is for the Linkedin Learning course: Learning C++
💫 Toolkit to help you get started with Spec-Driven Development
This repository contains the source code for Bayesian Learned Interatomic Potentials (BLIP)
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
✨✨Latest Advances on Multimodal Large Language Models
Practically and asymptotically accurate conditional sampling from diffusion generative models without conditional training
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2
Paper list for equivariant neural network
Bayesian Optimization Hackathon for Chemistry and Materials, Project 15: Adaptive Batch Sizes for Bayesian Optimization of Reaction Yield
A python implementation of the concepts in the book "Reinforcement Learning: An Introduction" by R.S. Sutton and A. G. Barto.
Implementing the value iteration algorithm for gridworld
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Chemistry-related Python utilities used in the RXN universe
Building blocks for foundation models.
The code corresponding to Predictive Minisci Late Stage Functionalization with Transfer Learning
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
Understanding Deep Learning - Simon J.D. Prince
Artificial Intelligence Research for Science (AIRS)
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics