Python module for quantum chemistry

strip output from Jupyter and IPython notebooks

🎨 A succinct matplotlib wrapper for making beautiful, publication-quality graphics

A Library for Uncertainty Quantification.

SchNetPack - Deep Neural Networks for Atomistic Systems

sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has …

End-To-End Molecular Dynamics (MD) Engine using PyTorch

Console vcard client

A PyTorch implementation of L-BFGS.

A simple and flexible code for Reservoir Computing architectures like Echo State Networks

Gaussian processes in JAX and Flax.

TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained …

Laplace approximations for Deep Learning.

Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡

Python package for numerical derivatives and partial differential equations in any number of dimensions.

Line-granularity, thread-aware deterministic and statistic pure-python profiler

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

OpenML AutoML Benchmarking Framework

Deep learning quantum Monte Carlo for electrons in real space

Uncertainty quantification with PyTorch

The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.

An open-source Python package for creating fast and accurate interatomic potentials.

The tiniest of Gaussian Process libraries

Generic implementation for clustering with deep learning : representation learning (DNN) + clustering

A toolkit for visualizations in materials informatics.

ELFI - Engine for Likelihood-Free Inference

Deploy Dask on job schedulers like PBS, SLURM, and SGE

This project allows images to be automatically grouped into like clusters using a combination of machine learning techniques.

Gaussian Processes for Experimental Sciences