Quotient Complex Transformer (QCformer) for Perovskite Data Analysis

TL;DR

This paper introduces QCformer, a novel Transformer model utilizing quotient complex representations to better capture the complex interactions and periodicity in perovskite materials, leading to improved property prediction.

Contribution

The paper presents a new quotient complex-based representation and a Transformer model that effectively models higher-order interactions and periodicity in perovskite data.

Findings

QCformer outperforms existing models on HOIP property prediction datasets.

The quotient complex representation captures higher-order and periodic interactions.

Pretraining on benchmark datasets enhances model performance.

Abstract

The discovery of novel functional materials is crucial in addressing the challenges of sustainable energy generation and climate change. Hybrid organic-inorganic perovskites (HOIPs) have gained attention for their exceptional optoelectronic properties in photovoltaics. Recently, geometric deep learning, particularly graph neural networks (GNNs), has shown strong potential in predicting material properties and guiding material design. However, traditional GNNs often struggle to capture the periodic structures and higher-order interactions prevalent in such systems. To address these limitations, we propose a novel representation based on quotient complexes (QCs) and introduce the Quotient Complex Transformer (QCformer) for material property prediction. A material structure is modeled as a quotient complex, which encodes both pairwise and many-body interactions via simplices of varying dimensions and captures material periodicity through a quotient operation. Our model leverages higher-order features defined on simplices and processes them using a simplex-based Transformer module. We pretrain QCformer on benchmark datasets such as the Materials Project and JARVIS, and fine-tune it on HOIP datasets. The results show that QCformer outperforms state-of-the-art models in HOIP property prediction, demonstrating its effectiveness. The quotient complex representation and QCformer model together contribute a powerful new tool for predictive modeling of perovskite materials.