Machine Learning Nucleation Collective Variables with Graph Neural Networks

06 October 2023, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs, which enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems by using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs.


Collective Variables
Graph Neural Networks
Enhanced Sampling

Supplementary materials

Supplementary Material
Additional discussion of FES calculation from metadynamics and reweighing based on Mean Force Integration.

Supplementary weblinks


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