mlcolvar 1.0.1

Machine learning collective variables for enhanced sampling

Machine Learning Collective Variables for Enhanced Sampling

CODE

PAPER

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mlcolvar is a Python library aimed to help design data-driven collective-variables (CVs) for enhanced sampling simulations. The key features are:

1. A unified framework to help test and use (some) of the CVs proposed in the literature.
2. A modular interface to simplify the development of new approaches and the contamination between them.
3. A streamlined distribution of CVs in the context of advanced sampling.

The library is built upon the PyTorch ML library as well as the Lightning high-level framework.

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Some of the CVs which are implemented, organized by learning setting:

• Unsupervised: PCA, (Variational) AutoEncoders [1,2]
• Supervised: LDA [3], DeepLDA [4], DeepTDA [5]
• Time-informed: TICA [6], DeepTICA/SRVs [7,8], VDE [9]

And many others can be implemented based on the building blocks or with simple modifications. Check out the documentation and the examples section!

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PLUMED interface: the resulting CVs can be deployed for enhancing sampling with the PLUMED package via the pytorch interface, available since version 2.9.

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Notes: in early versions (v<=0.2.*) the library was called mlcvs. This is still accessible for compatibility with PLUMED masterclasses in the releases or by cloning the pre-lightning branch.

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Copyright (c) 2023 Luigi Bonati, Enrico Trizio, Andrea Rizzi and Michele Parrinello. Structure of the project is based on Computational Molecular Science Python Cookiecutter.