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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. [ABSTRACT FROM AUTHOR]

A new method for the synthesis of the tripeptide part of the jaspamide and chondramide alkaloids using a Ugi reaction was developed. The reported approach is considerably shorter than all literature syntheses of the peptide parts of these natural products. A family of peptides with different substituents at the 2‐position of the indole moiety was prepared to open up access to analogues of these natural products. [ABSTRACT FROM AUTHOR]

The article presents a study which explores about the accurate global machine learning force fields for molecules with hundreds of atoms. It mentions that robustness of the approach is demonstrated in nanosecond path-integral molecular dynamics simulations for supramolecular complexes in the MD22 dataset.