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 language | English |
|---|---|
| Pages (from-to) | 9898-9908 |
| Number of pages | 11 |
| Journal | Journal of Physical Chemistry C |
| Volume | 126 |
| Issue number | 23 |
| Early online date | 1 Jun 2022 |
| DOIs | |
| Publication status | Published - 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.
| Funders | Funder number |
|---|---|
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | 680–91-086 |
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 6 Clean Water and Sanitation
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