CUED Publications database

A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Deringer, V and Caro, M and Csanyi, G A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nature Communications, 11. ISSN 2041-1723 (Unpublished)

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Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an out- standing challenge. Here we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum- mechanical results. Our model is fitted to density-functional theory plus many-body dis- persion (DFT+MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of “phosphorene” and “hittorfene”); its transfer- ability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale ex- emplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chem- istry, physics, and materials science.

Item Type: Article
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 23 Sep 2020 22:32
Last Modified: 02 Mar 2021 06:54
DOI: 10.1038/s41467-020-19168-z