Lead-free perovskites for next-generation applications: a comprehensive computational and data-driven review

Lead-free perovskites (LFPs) are an emergent class of materials with great potential as next-generation candidates for energy and optoelectronic applications, offering a sustainable and non-toxic alternative to their lead-based counterparts. Computational studies play a central role in accelerating the discovery, design, and optimization of these materials by enabling predictive insights into electronic, optical, and device-level behavior. This review presents a comprehensive analysis of the computational landscape surrounding lead-free perovskites, combining bibliometric mapping, methodological classification, and thematic exploration across material types and application domains. A total of 200 peer-reviewed articles published between 2013 and 2025 were analyzed, offering a comprehensive picture of how computational tools from density functional theory (DFT) to machine learning (ML), and device-level simulation have shaped LFP research. The review highlights the dominant role of photovoltaic modeling and the growing diversification of lead-free perovskite research into applications such as thermoelectrics, spintronics, photocatalysis, neuromorphic computing, radiation detection, thermal barrier coatings, gas sensing, and ferroelectric systems. Density functional theory remains the foundational tool, supported by increasingly sophisticated approaches such as high-throughput screening and device-level simulation. The novelty of this study lies in its data-driven, cross-scale synthesis that links computational strategies to targeted properties and application outcomes of lead-free perovskites. It outlines strategic initiatives through which theory and simulations have driven the discovery and optimization of high-performance, stable LFPs, while identifying emerging trends and future directions in the evolving role of computational science in materials innovation.