Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials

TL;DR

This paper presents a comprehensive computational screening and machine learning approach to identify and analyze Rashba-Dresselhaus splitting and optoelectronic properties in over 2,000 two-dimensional perovskite materials, facilitating targeted material discovery.

Contribution

It introduces a multiscale computational workflow and a high-throughput framework for Rashba-Dresselhaus splitting, along with machine learning models for property prediction in 2D perovskites.

Findings

Generated a dataset of over 2,000 2D perovskites with diverse compositions.

Developed a framework for high-throughput Rashba-Dresselhaus splitting computation.

Trained machine learning models for band gap and property prediction.

Abstract

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices, and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2,000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.