  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.
