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Classical Force-Field Parameters for CsPbBr3Perovskite Nanocrystals

  • Roberta Pascazio
  • , Francesco Zaccaria
  • , Bas Van Beek
  • , Ivan Infante*
  • *Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Understanding the chemico-physical properties of colloidal semiconductor nanocrystals (NCs) requires exploration of the dynamic processes occurring at the NC surfaces, in particular at the ligand-NC interface. Classical molecular dynamics (MD) simulations under realistic conditions are a powerful tool to acquire this knowledge because they have good accuracy and are computationally cheap, provided that a set of force-field (FF) parameters is available. In this work, we employed a stochastic algorithm, the adaptive rate Monte Carlo method, to optimize FF parameters of cesium lead halide perovskite (CsPbBr3) NCs passivated with typical organic molecules used in the synthesis of these materials: oleates, phosphonates, sulfonates, and primary and quaternary ammonium ligands. The optimized FF parameters have been obtained against MD reference trajectories computed at the density functional theory level on small NC model systems. We validated our parameters through a comparison of a wide range of nonfitted properties to experimentally available values. With the exception of the NC-phosphonate case, the transferability of the FF model has been successfully tested on realistically sized systems (>5 nm) comprising thousands of passivating organic ligands and solvent molecules, just as those used in experiments.

Original languageEnglish
Pages (from-to)9898-9908
Number of pages11
JournalJournal of Physical Chemistry C
Volume126
Issue number23
Early online date1 Jun 2022
DOIs
Publication statusPublished - 16 Jun 2022

Bibliographical note

Funding Information:
Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Programme 2017 grant from the Netherlands Organization of Scientific Research (NWO)with number 680–91-086

Funding Information:
I.I. and B.v.B. acknowledge The Netherlands Organization of Scientific Research (NWO) for financial support through the Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Program 2017 grant with the number 680-91-086. The computational work was carried out on the Dutch national e-infrastructure with support of the SURFCooperative and on the Italian National Supercomputer CINECA (Casalecchio di Reno, Italy), through the ISCRA Project IsC83_PERDOT.

Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.

Funding

Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Programme 2017 grant from the Netherlands Organization of Scientific Research (NWO)with number 680–91-086 I.I. and B.v.B. acknowledge The Netherlands Organization of Scientific Research (NWO) for financial support through the Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Program 2017 grant with the number 680-91-086. The computational work was carried out on the Dutch national e-infrastructure with support of the SURFCooperative and on the Italian National Supercomputer CINECA (Casalecchio di Reno, Italy), through the ISCRA Project IsC83_PERDOT.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek680–91-086
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 6 - Clean Water and Sanitation
      SDG 6 Clean Water and Sanitation

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