Mechanical Properties of Nanoporous Graphenes

Transferability of Graph Machine-Learned Force Fields Compared to Local and Reactive Potentials

verfasst von

Adil Kabylda, Bohayra Mortazavi, Xiaoying Zhuang, Alexandre Tkatchenko

Abstract

Nanoporous and chemically-bridged graphene nanosheets span a wide chemical space with a broad set of applications in sensing and electronics. Modeling the structure and dynamics of such nanosheets is challenging, as chemical bond making and breaking as well as non-covalent interactions must be captured accurately and on equal footing. Here it is showed that recent graph-based machine-learned force field (MLFF) SO3krates [J. T. Frank et al., Nat. Commun. 15, 6539 (2024)] is able to reliably model the dynamics and mechanical response for a broad class of nanoporous graphenes when trained on accurate density functional theory data that includes long-range many-body dispersion (MBD) interactions. In contrast, local moment tensor potentials and empirical reactive potentials are much less accurate. It is also found that recent MLFFs trained on solid-state datasets must be used with care, since even empirical potentials occasionally yield more accurate results. These findings highlight the potential of properly-trained graph MLFFs in modeling the properties of whole chemical spaces of complex functional materials.

Organisationseinheit(en)

Institut für Photonik
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme

Externe Organisation(en)

University of Luxembourg

Typ

Artikel

Journal

Advanced functional materials

Band

35

Anzahl der Seiten

9

ISSN

1616-301X

Publikationsdatum

24.03.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Elektronische, optische und magnetische Materialien, Allgemeine Chemie, Biomaterialien, Allgemeine Materialwissenschaften, Physik der kondensierten Materie, Elektrochemie

Elektronische Version(en)

https://doi.org/10.1002/adfm.202417891 (Zugang: Offen)