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A machine learning algorithm for studying how molecules self-assemble and function

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A machine learning algorithm has been developed to capture and analyze rare molecular processes, revealing how molecules self-organize and function. The algorithm is general and can be applied whenever a dynamic system has a notion of ‘likely fate’.

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Fig. 1: Learning assembly mechanisms from path sampling.

References

  1. Peters, B. Reaction Rate Theory and Rare Events Simulations (Elsevier, 2017). A textbook on rare events and how to simulate them.

  2. Dellago, C., Bolhuis, P. G. & Geissler, P. L. Transition path sampling. Adv. Chem. Phys. 123, 1–78 (2002). A review article that presents transition path sampling.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Jung, H. et al. Machine-guided path sampling to discover mechanisms of molecular self-organization. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00428-z (2023).

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A machine learning algorithm for studying how molecules self-assemble and function. Nat Comput Sci 3, 289–290 (2023). https://doi.org/10.1038/s43588-023-00441-2

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  • DOI: https://doi.org/10.1038/s43588-023-00441-2

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