Data about the transition states of rare transitions between long-lived states are needed to simulate physical and chemical processes; however, existing computational approaches often gather little information about these states. A machine-learning technique resolves this challenge by exploiting the century-old theory of committor functions.

Trizio, E., Kang, P., Parrinello, M. (2024). Systematic simulations and analysis of transition states using committor functions. NATURE COMPUTATIONAL SCIENCE, 4(6), 396-397 [10.1038/s43588-024-00652-1].

Systematic simulations and analysis of transition states using committor functions

Trizio, E
;
2024

Abstract

Data about the transition states of rare transitions between long-lived states are needed to simulate physical and chemical processes; however, existing computational approaches often gather little information about these states. A machine-learning technique resolves this challenge by exploiting the century-old theory of committor functions.
Editoriale, introduzione, contributo a forum/dibattito
Committor, Transition State Ensemble, Enhanced Sampling
English
5-giu-2024
2024
4
6
396
397
reserved
Trizio, E., Kang, P., Parrinello, M. (2024). Systematic simulations and analysis of transition states using committor functions. NATURE COMPUTATIONAL SCIENCE, 4(6), 396-397 [10.1038/s43588-024-00652-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/524312
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