A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$

Abstract

Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality reduction, classification of metastable states or identification of slow modes. Here we present $\text{mlcolvar}$, a Python library that simplifies the construction of these variables and their use in the context of enhanced sampling through a contributed interface to the PLUMED software. The library is organized modularly to facilitate the extension and cross-contamination of these methodologies. In this spirit, we developed a general multi-task learning framework in which multiple objective functions and data from different simulations can be combined to improve the collective variables. The library's versatility is demonstrated through simple examples that are prototypical of realistic scenarios.

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Cited by in corpus (10)

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• Structure and polymerization of liquid sulfur across the $\lambda$-transition
• Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
• Learning Markovian Dynamics with Spectral Maps
• Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach
• Guest Editorial: Special Topic on Software for Atomistic Machine Learning
• Fast Sampling of Protein Conformational Dynamics