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Microsoft Quantum
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Highlights
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Tools for diffing and merging of Jupyter notebooks.
Quantum-inspired Cluster Expansion: formulating chemical space search as QUBOs and Ising models
Heat-conductivity benchmark test for foundational machine-learning potentials
Library for reading and writing chemistry files
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A Python library and command line interface for automated free energy calculations
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
Standard input/output package for AtomsBase-compatible structures
Computing representations for atomistic machine learning
An open library for the analysis of molecular dynamics trajectories
Open-source scientific and technical publishing system built on Pandoc.
A simple, robust and flexible just-in-time job management framework in Python.
Code for automated fitting of machine learned interatomic potentials.
Typer, build great CLIs. Easy to code. Based on Python type hints.
Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.
This workflow aims to generate configurations to analyze the thermodynamic properties of binary alloys.
Web-based MongoDB admin interface, written with Node.js and Express
Universal Python binding for the LMDB 'Lightning' Database
JupyterLab desktop application, based on Electron.
Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei, heatmaps, scatter plots.
A toolkit for visualizations in materials informatics.
A community-maintained Python framework for creating mathematical animations.
WebGL accelerated JavaScript molecular graphics library
High-throughput workflows to calculate surface energies of solids.