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 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.
A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar
Note: This paper is part of the JCP Special Topic on Software for Atomistic Machine Learning.
Luigi Bonati, Enrico Trizio, Andrea Rizzi, Michele Parrinello; A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar. J. Chem. Phys. 7 July 2023; 159 (1): 014801. https://doi.org/10.1063/5.0156343
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