Learning data-efficient coarse-grained molecular dynamics from forces and noise.
Saved in:
| Title: | Learning data-efficient coarse-grained molecular dynamics from forces and noise. |
|---|---|
| Authors: | Durumeric AEP; Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany., Chen Y; Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany., Pasos-Trejo AS; Department of Physics, Freie Universität Berlin, Berlin, Germany., Noé F; Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. frank.noe@fu-berlin.de.; Department of Physics, Freie Universität Berlin, Berlin, Germany. frank.noe@fu-berlin.de.; AI4Science, Microsoft Research, Berlin, Germany. frank.noe@fu-berlin.de.; Department of Chemistry, Rice University, Houston, TX, USA. frank.noe@fu-berlin.de., Clementi C; Department of Chemistry, Rice University, Houston, TX, USA. cecilia.clementi@fu-berlin.de.; Center for Theoretical Biological Physics, Rice University, Houston, TX, USA. cecilia.clementi@fu-berlin.de. |
| Source: | Nature communications [Nat Commun] 2026 Mar 15; Vol. 17 (1). Date of Electronic Publication: 2026 Mar 15. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 2041-1723 |
|---|---|
| DOI: | 10.1038/s41467-026-70818-0 |
