Geometric data analysis-based machine learning for two-dimensional perovskite design

Abstract

With extraordinarily high efficiency, low cost, and excellent stability, 2D perovskite has demonstrated a great potential to revolutionize photovoltaics technology. However, inefficient material structure representations have significantly hindered artificial intelligence (AI)-based perovskite design and discovery. Here we propose geometric data analysis (GDA)-based perovskite structure representation and featurization and combine them with learning models for 2D perovskite design. Both geometric properties and periodicity information of the material unit cell, are fully characterized by a series of 1D functions, i.e., density fingerprints (DFs), which are mathematically guaranteed to be invariant under different unit cell representations and stable to structure perturbations. Element-specific DFs, which are based on different site combinations and atom types, are combined with gradient boosting tree (GBT) model. It has been found that our GDA-based learning models can outperform all existing models, as far as we know, on the widely used new materials for solar energetics (NMSE) databank.