ArticleJournal of chemical theory and computation2024
Machine Learning Nucleation Collective Variables with Graph Neural Networks.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Hierarchical geometric deep learning enables scalable analysis of molecular dynamics.The Journal of chemical physics · 2026Article
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Making PLUMED Fly: A Tutorial on Optimizing Performance.The journal of physical chemistry. B · 2026Article
- Committors without Descriptors.Journal of chemical theory and computation · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Deciphering the complexities of crystalline state(s) with molecular simulations.Communications chemistry · 2025Review
- Machine Learning Classification of Local Environments in Molecular Crystals.Journal of chemical theory and computation · 2024Article
- Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 that 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 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.