Machine Learning-Aided Property Prediction of Hybrid Organic-Inorganic Perovskites Using Hirshfeld Surface Representations of Crystal Structures

This paper describes a lightweight neural network (NN) to predict thermodynamic, electric, and electronic properties of hybrid organic-inorganic perovskites (HOIPs) using Hirshfeld surfaces as novel material representation for HOIPs. The neural network utilizes only a few Hirshfeld surface features (e.g., volume, surface area, globularity, and effective radius), along with qualitative and quantitative (mixed) variables, to predict the properties of HOIPs in a highly accelerated manner. Our use of Hirshfeld surface-based descriptors of HOIP crystals leads to a new metric for measuring the effective radius of an organic molecule within a given structure, which are proven to be highly effective features for efficient machine learning of crystalline materials’ properties. A detailed comparison between the crystal graph convolutional neural network (CGCNN) and the Hirshfeld surface-based neural network analysis via UMAP and HDBSCAN clustering is provided to assess the efficacy of these methods for different compound chemistries. It is shown that a combination of lower-order feature representation and a shallow lightweight neural network is capable of predicting material properties for HOIPs. Benchmarking against well-established denser crystal property prediction techniques such as the CGCNN and deeper graph attention layer graph neural network (deeper GATGNN) shows that our approach provides comparable and, in some cases, even superior predictive performance of properties such as formation energy, band gap, and electronic dielectric constant but all at much lower computational cost.