The development of perovskites and perovskite-inspired materials (PIMs) is driven by the need for efficient, non-toxic and stable solar energy conversion technologies. While halide perovskites exhibit outstanding optoelectronic properties, their practical deployment remains hindered by toxicity concerns and long-term instability. Conventional experimental and computational approaches, though effective, are often limited by high costs and low throughput, prompting the need for data-driven strategies. In this review, we provide a comprehensive analysis of machine learning (ML)-driven approaches for predicting key properties such as bandgap, stability, and lattice constants in perovskite and PIMs systems. We outline a complete ML workflow, from target identification and data collection to feature engineering and model selection across supervised, unsupervised, and reinforcement learning frameworks. Special attention is given to the transferability of ML strategies developed for halide perovskites to the more chemical diverse PIMs landscape. By highlighting recent progress and current limitations, we provide a critical roadmap for integrating ML into the rational design and discovery of next-generation non-toxic, stable solar materials. These insights are expected to accelerate the discovery-to-deployment cycle for low-toxicity, high-efficiency solar absorbers and catalyze innovation across the broader field of data-driven energy materials.
Zhang et al. (2026) studied this question.