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’.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Peters, B. Reaction Rate Theory and Rare Events Simulations (Elsevier, 2017). A textbook on rare events and how to simulate them.
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.
Additional information
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).
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00441-2
