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- W4385667154 abstract "Semiconducting nanocrystals passivated with organic ligands have emerged as a powerful platform for light harvesting, light-driven chemical reactions, and sensing. Due to their complexity and size, little structural information is available from experiments, making these systems challenging to model computationally. Here, we develop a machine-learned force field trained on DFT data and use it to investigate the surface chemistry of a PbS nanocrystal interfaced with acetate ligands. In doing so, we go beyond considering individual local minimum energy geometries and, importantly, circumvent a precarious issue associated with the assumption of a single assigned atomic partial charge for each element in a nanocrystal, independent of its structural position. We demonstrate that the carboxylate ligands passivate the metal-rich surfaces by adopting a very wide range of “tilted-bridge” and “bridge” geometries and investigate the corresponding ligand IR spectrum. This work illustrates the potential of machine-learned force fields to transform computational modeling of these materials." @default.
- W4385667154 created "2023-08-09" @default.
- W4385667154 creator A5000410825 @default.
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- W4385667154 date "2023-08-08" @default.
- W4385667154 modified "2023-10-09" @default.
- W4385667154 title "Exploring Configurations of Nanocrystal Ligands Using Machine-Learned Force Fields" @default.
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- W4385667154 doi "https://doi.org/10.1021/acs.jpclett.3c01618" @default.
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