qmlcode/qmllib
Quantum machine learning (QML) molecular representations and core functions
This toolkit helps computational chemists and materials scientists apply machine learning to predict properties of molecules and solids. It takes molecular structures as input and outputs optimized numerical representations and core functions, which can then be used in machine learning models to predict various chemical or material properties. It's designed for researchers building their own quantum machine learning workflows.
Available on PyPI.
Use this if you are a computational scientist who needs efficient, low-level building blocks to create custom quantum machine learning models for molecular and solid-state properties.
Not ideal if you are looking for a high-level, 'plug-and-play' machine learning framework like scikit-learn that handles model training and prediction automatically.
Stars
24
Forks
5
Language
Python
License
MIT
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
Dependencies
1
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