Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories-Reference-Cited by-同舟云学术

Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories

Published:2023-08-30 Issue:35 Volume:14 Page:7910-7923
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Seckler Henrik¹^ORCID,Szwabiński Janusz²,Metzler Ralf¹³^ORCID

Affiliation:

1. Institute of Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany

2. Hugo Steinhaus Center, Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

3. Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea

Funder

Bundesministerium f?r Bildung und Forschung

Narodowe Centrum Nauki

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.3c01351

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3. Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

4. Anomalous transport in the crowded world of biological cells

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