Quotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design

With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Effective data representations are key to the success of all learning models. Currently, the lack of comprehensive and accurate material representations has hindered AI-based design and discovery of 2D perovskites, limiting their potential for advanced photovoltaic applications. In this context, this work introduces a novel computational topology framework termed the quotient complex (QC), which serves as the foundation for the material representation. The proposed 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, the proposed 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.