Discovery of Stable Hybrid Organic-inorganic Double Perovskites for High-performance Solar Cells via Machine-learning Algorithms and Crystal Graph Convolution Neural Network Method

TL;DR

This study employs machine learning and crystal graph convolution neural networks to identify stable, high-performance hybrid organic-inorganic double perovskites suitable for solar cells, accelerating materials discovery.

Contribution

It introduces a combined ML and CGCNN approach for screening and predicting properties of double perovskites, demonstrating its effectiveness in identifying promising photovoltaic materials.

Findings

Six out of twenty candidates have suitable bandgaps for solar applications.

The combined ML and CGCNN method accurately predicts key properties of perovskites.

Identified novel perovskite candidates with potential for high-performance solar cells.

Abstract

Hybrid peroskite solar cells are newly emergent high-performance photovoltaic devices, which suffer from disadvantages such as toxic elements, short-term stabilities, and so on. Searching for alternative perovskites with high photovoltaic performances and thermally stabilities is urgent in this field. In this work, stimulated by the recently proposed materials-genome initiative project, firstly we build classical machine-learning algorithms for the models of formation energies, bangdaps and Deybe temperatures for hybrid organic-inorganic double perovskites, then we choose the high-precision models to screen a large scale of double-perovskite chemical space, to filter out good pervoskite candidates for solar cells. We also analyze features of importances for the the three target properties to reveal the underlying mechanisms and discover the typical characteristics of high-performances double perovskites. Secondly we adopt the Crystal graph convolution neural network (CGCNN), to build precise model for bandgaps of perovskites for further filtering. Finally we use the ab-initio method to verify the results predicted by the CGCNN method, and find that, six out of twenty randomly chosen (CH3)2NH2-based HOIDP candidates possess finite bandgaps, and especially, (CH3)2NH2AuSbCl6 and (CH3)2NH2CsPdF6 possess the bandgaps of 0.633 eV and 0.504 eV, which are appropriate for photovoltaic applications. Our work not only provides a large scale of potential high-performance double-perovskite candidates for futural experimental or theoretical verification, but also showcases the effective and powerful prediction of the combined ML and CGCNN method proposed for the first time here.