Two-dimensional (2D) halide perovskites are promising semiconductor materials for a wide range of optoelectronic applications due to their unique properties, including high carrier mobility, solution processibility, and strong light absorption. 2D hybrid perovskite materials provide a much larger design space of organic ligand chemistries in comparison with bulk 3D materials. Many recent reports have highlighted how tuning the ligand chemistry can impact the optoelectronic properties and stability profiles of the resulting 2D materials.<br/>However, what molecular features of the spacer cation can stabilize 2D perovskites remains unknown. So far, the synthesis of newly-designed ligand is based on trial-and-error tests with limited ability to predict the resulting structure which slows down the discovery of novel materials.<br/>In this work, we have established a high-throughput molecular simulation framework screen prospective ligand chemistries for synthesizability and stabilization of the perovskite lattice. In addition, machine learning models are used to learn the features that dictate the stability of 2D perovskites based on tens of thousands of perovskite materials screened by our framework. We also present a few case studies of new materials that have been synthesized based on the prediction of our framework and the model.

Novel ligand design of 2D halide perovskites through molecular dynamics and machine learning model

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.