Machine-guided path sampling to discover mechanisms of molecular self-organization

Autor(en)

Hendrik Jung, Roberto Covino, A. Arjun, Christian Leitold, Christoph Dellago, Peter G. Bolhuis, Gerhard Hummer

Abstrakt

Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Here we present an autonomous path sampling algorithm that integrates deep learning and transition path theory to discover the mechanism of molecular self-organization phenomena. The algorithm uses the outcome of newly initiated trajectories to construct, validate and—if needed—update quantitative mechanistic models. Closing the learning cycle, the models guide the sampling to enhance the sampling of rare assembly events. Symbolic regression condenses the learned mechanism into a human-interpretable form in terms of relevant physical observables. Applied to ion association in solution, gas-hydrate crystal formation, polymer folding and membrane-protein assembly, we capture the many-body solvent motions governing the assembly process, identify the variables of classical nucleation theory, uncover the folding mechanism at different levels of resolution and reveal competing assembly pathways. The mechanistic descriptions are transferable across thermodynamic states and chemical space.

Organisation(en)

Computergestützte Physik und Physik der Weichen Materie

Externe Organisation(en)

Max-Planck-Institut für Biophysik, Frankfurt Institute for Advanced Studies (FIAS), University of Amsterdam (UvA), Johann Wolfgang Goethe-Universität Frankfurt am Main

Journal

Nature Computational Science

Band

3

Seiten

334–345

Anzahl der Seiten

12

ISSN

2662-8457

DOI

https://doi.org/10.1038/s43588-023-00428-z

Publikationsdatum

04-2023

Peer-reviewed

Ja

ÖFOS 2012

103006 Chemische Physik, 103043 Computational Physics, 103029 Statistische Physik

ASJC Scopus Sachgebiete

Computer Science (miscellaneous), Computer Science Applications, Computer Networks and Communications

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/c22963ee-0f55-41ef-a8d2-a34ac9d57ca5