IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals: Simulation and Experiment
arXiv:2403.18779 · doi:10.1021/acs.jpcc.4c01988
Abstract
Surfaces of colloidal nanocrystals are frequently passivated with carboxylate ligands which exert significant effects on their optoelectronic properties and chemical stability. Experimentally, binding geometries of such ligands are typically investigated using vibrational spectroscopy, but the interpretation of the IR signal is usually not trivial. Here, using machine-learning (ML) algorithms trained on DFT data, we simulate an IR spectrum of a lead-rich PbS nanocrystal passivated with butyrate ligands. We obtain a good agreement with the experimental signal and demonstrate that the observed line shape stems from a very wide range of `tilted-bridge'-type geometries and does not indicate a coexistence of `bridging' and `chelating' binding modes as has been previously assumed. This work illustrates limitations of empirical spectrum assignment and demonstrates the effectiveness of ML-driven molecular dynamics simulations in reproducing IR spectra of nanoscopic systems.
arXiv commentSubmitted. SI available on request
References in corpus (6)
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- NWChem: Past, Present, and Future
- DeePMD-kit v2: A software package for Deep Potential models
- Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
- Deep Potentials for Materials Science