SO3krates and Universal Pairwise Force Field for Molecular Simulation

ORB forcefield models from Orbital Materials

Universal Tensor Operations in Einstein-Inspired Notation for Python.

A massively parallel, high-level programming language

An interface to various automatic differentiation backends in Julia.

Reference implementation of "Ewald-based Long-Range Message Passing for Molecular Graphs" (ICML 2023)

A flexible and performant framework for training machine learning potentials.

tools for graph-based machine-learning potentials in jax

A dataset for benchmarking non-local capabilities of geometric machine learning models.

Differentiation for Hackers

A collection of modern/faster/saner alternatives to common unix commands.

Physics simulation software for space + aerospace

Atomistic machine learning library for training interatomic potentials within PyTorch and PyTorch Geometric frameworks

Deep learning at the speed of light.

Build neural networks for machine learning force fields with JAX

Density-functional toolkit

Calculation of Wigner symbols and related constants

Multiple dispatch over abstract array types in JAX.

E3x is a JAX library for constructing efficient E(3)-equivariant deep learning architectures built on top of Flax.

Neural Networks for JAX

Hardware accelerated, batchable and differentiable optimizers in JAX.

Differentiate all the things!

Julia Programming for Machine Learning course at TU Berlin

Multi-language library for the calculation of spherical harmonics in Cartesian coordinates

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.