Exploring Configurations of Nanocrystal Ligands Using Machine-Learned Force Fields

• Published in: The journal of physical chemistry letters 2023-08, Vol.14 (32), p.7215-7222
• Main Authors: Sowa, Jakub K., Roberts, Sean T., Rossky, Peter J.
• Format: Article
• Language: English
• Subjects: Physical Insights into Chemistry, Catalysis, and Interfaces
• ISSN: 1948-7185; 1948-7185
• DOI: 10.1021/acs.jpclett.3c01618

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.