Abstract
Colloidal epitaxial heterostructures are nanoparticles composed of two different materials connected at an interface, which can exhibit properties different from those of their individual components. The ability to combine dissimilar materials offers wide opportunities to create functional heterostructures. However, the design stage often focuses on combining materials based on the desired properties, while structural compatibility at the interface is overlooked. To accelerate the design of new heterostructures between ionic materials, which encompass most colloidal semiconductors, we implemented a new workflow in the Ogre code for the prediction of epitaxial interfaces. Thanks to a pre-screening of candidate models based on charge balance and an electrostatic force-field for fast energy evaluations, our workflow can optimize complex interfaces in just a few minutes on a simple laptop. We validate our approach for heterostructures involving lead halide perovskites, for which Ogre produces interface models in excellent agreement with the experiments. Further case studies demonstrate how Ogre can be used to (re-)interpret experimental data and propose atomistic models for previously unknown interfaces involving ionic materials, such as metal halides and oxides. The Ogre package is available on GitHub, and users without computational expertise can run it via the OgreInterface desktop application, available for Windows, Linux, and Mac.
Supplementary materials
Title
Supporting Information for Fast Prediction of Ionic Epitaxial Interfaces with Ogre Demonstrated for Colloidal Heterostructures of Lead Halide Perovskites
Description
Supporting Information document for the manuscript entitled "Fast Prediction of Ionic Epitaxial Interfaces with Ogre Demonstrated for Colloidal Heterostructures of Lead Halide Perovskites", authored by Stefano Toso, Derek Dardzinski, Liberato Manna and Noa Marom. It contains the full results of all simulations discussed in the Main Text, plus an in-depth discussion of the technical solutions adopted in the Ogre prediction workflow.
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Supplementary weblinks
Title
GitHub repository of the OgreInterface library
Description
Link to the GitHub repository hosting the version of the OgreInterface library used in this work.
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Supporting Material database
Description
A database containing the raw results of all Ogre simulations, the Python scripts needed to reproduce them, and the installation wizards of the OgreInterface desktop application (for Windows, Linux, and Mac).
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