Quotient complex (QC)-based machine learning for 2D perovskite design

TL;DR

This paper introduces a novel quotient complex (QC) framework for representing 2D perovskites, enabling improved machine learning predictions of their properties by capturing higher-order interactions and periodicity.

Contribution

The paper presents the quotient complex (QC) framework and descriptors (QCDs) for 2D perovskite representation, enhancing machine learning models over existing methods.

Findings

QC-based models outperform existing approaches

Periodic boundary conditions improve prediction accuracy

Higher-order interactions are effectively encoded

Abstract

With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Presently, inadequate representations have substantially impeded the design and discovery of 2D perovskites. In this context, we introduce a novel computational topology framework termed the quotient complex (QC), which serves as the foundation for the material representation. Our QC-based features are seamlessly integrated with learning models for the advancement of 2D perovskite design. At the heart of this framework lies the quotient complex descriptors (QCDs), representing a quotient variation of simplicial complexes derived from materials unit cell and periodic boundary conditions. Differing from prior material representations, this approach encodes higher-order interactions and periodicity information simultaneously. Based on the well-established New Materials for Solar Energetics (NMSE) databank, our QC-based machine learning models exhibit superior performance against all existing counterparts. This underscores the paramount role of periodicity information in predicting material functionality, while also showcasing the remarkable efficiency of the QC-based model in characterizing materials structural attributes.