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Path Sampling for Rare Events Boosted by Machine Learning

Porhouy Minh¹; Sapna Sarupria^1
1Department of Chemistry and Chemical Theory Center, University of Minnesota Minneapolis, MN USA

Commentary on

H. Jung, R. Covino, A. Arjun, C. Leitold, C. Dellago, P.G. Bolhuis and G. Hummer, “Machine-guided path sampling to discover mechanisms of molecular self-organization”, Nat. Comput. Sci. 3, 334–345 (2023), https://doi.org/10.1038/s43588-023-00428-z

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Statement of Significance

The study by Jung et al. introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method’s potential impact and limitations.

How to cite

Cite the commentary as:

P. Minh & S. Sarupria, "Path Sampling for Rare Events Boosted by Machine Learning",
KIM REVIEW, Volume 4, Article 01, 2026. DOI: 10.25950/7f47b6e6