Metal halide perovskite materials have shown significant advancements in their application as light absorbers in perovskite solar cells, with power conversion efficiencies reaching 27%. However, lead-based perovskites pose a concern due to their toxicity and stability issues in moisture, UV radiation, and heat. This has led to a pressing need to explore substitute materials that do not contain lead but maintain the remarkable characteristics of lead-based perovskites. This review article focuses on halide double perovskites characterised by the A₂B’B”X₆ composition, highlighting their structural, optical, thermoelectric, and mechanical capabilities. Additionally, the review evaluates several materials databases to investigate materials suitable for high-throughput first-principles calculations integrated inside density functional theory. The review aims to identify novel perovskite materials, offer a thorough evaluation of the potential benefits and drawbacks associated with this class of materials, and, from the pedagogical standpoint, discover effective instructional frameworks.

Traditionally, the development and optimisation of photocatalytic materials have relied on experimental approaches and density functional theory (DFT) calculations. Although these methods have driven significant scientific progress, they are increasingly constrained by high computational costs, lengthy development cycles, and limited scalability. In recent years, machine learning (ML) has emerged as a powerful and sustainable alternative, offering a data-driven framework that accelerates materials discovery through rapid and accurate property prediction. This review highlights the essential components of the ML workflow data collection, feature engineering, model selection, and validation while exploring its application in predicting photocatalytic properties. It further discusses recent advances in forecasting key characteristics such as band edge positions, charge carrier mobility, and surface reactivity using both supervised and unsupervised ML techniques. Persistent challenges, including data scarcity, model interpretability, and generalisability, are also addressed, alongside potential strategies to improve the robustness and reliability of ML-driven materials design. By combining high prediction accuracy with superior computational efficiency, ML holds the potential to revolutionise high-throughput screening and guide the systematic development of next-generation photocatalysts.

The structural, electronic, mechanical, and thermoelectric properties of alkali-metal-based bismuth selenides (${ABiSe}_2$; $A$ = Na, K) are investigated using a combination of ab initio density functional theory and semiclassical Boltzmann transport theory. The computed lattice constants are in close agreement with experimental results obtained from the Crystallography Open Database. The calculation of the phonon dispersion, elastic tensor, and formation energy confirm that both compounds are dynamically, mechanically, and thermodynamically stable. The band gaps of $\mathrm{KBi}{\mathrm{Se}}_2$ and $\mathrm{NaBi}{\mathrm{Se}}_2$ are indirect, and their room-temperature values computed at the HSE06 level including spin-orbit coupling (SOC) are 1.20 and 1.13 eV, respectively. SOC interactions impact the band gaps of these compounds which in turn influence the optical and thermoelectric properties, respectively. The absorption coefficients and figure-of-merit values obtained from optical and thermoelectric calculations suggest these materials as potential candidates for photovoltaic and thermoelectric applications.

Electronic band edge positions show if a material can convert solar energy effectively. This is important for making clean fuels and environmental remediation.