Enhanced sampling methods aim to simulate rare physical and chemical reactive processes involving transitions between long-lived states. Existing methods often disproportionally sample either metastable or transition states. A machine-learning approach combines the strengths of these two cases to characterize entire rare events with the same thoroughness in a single calculation.
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References
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This is a summary of: Trizio, E. et al. Everything everywhere all at once: a probability-based enhanced sampling approach to rare events. Nat. Comput. Sci. https://doi.org/10.1038/s43588-025-00799-5 (2025).
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A committor-based method to uniformly sample rare reactive events. Nat Comput Sci 5, 522–523 (2025). https://doi.org/10.1038/s43588-025-00825-6
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DOI: https://doi.org/10.1038/s43588-025-00825-6
