Mehmet Erdem Günay

In this work, catalytic, electrocatalytic, and photocatalytic CO ₂ reduction processes, as the three most commonly studied transformative CO ₂ utilization routes, are evaluated and compared for their future commercialization potential.

This work presents a data‐driven assessment of CO ₂ electroreduction (CO ₂ R) performance trends toward sustainable carbon utilization to produce chemical feedstocks and renewable fuels, using an expanded dataset of 662 observations from 47 studies published between 2003 and 2025. To enable both performance benchmarking and selectivity mapping, the highest Faradaic efficiency (FE) and the most selective product were chosen as target variables, while 13 variables (e.g., cathode catalyst elements, preparation method, and support type) were used as descriptors. First, exploratory data analysis was conducted to quantify how the reported highest FE values evolved over time. This analysis revealed a clear improvement in performance, with the median of the highest FEs increasing from 32.0% (2003–2013) to 47.8% (2022–2025). Furthermore, comparative analysis revealed shifts in system configurations, including increased use of chemical dealloying and Nafion 212 membranes. Subsequently, SHAP analysis quantified the direction and magnitude of feature effects on FE, while parallel coordinate plots highlighted multivariable patterns governing product selectivity (e.g., CO, HCOOH, and C ₂ H ₄ ). Notably, key findings include a strong positive association between Sn atomic percentage and HCOOH FE, as well as the identification of preparation methods and electrolyte conditions linked to high C ₂ H ₄ and CH ₄ selectivity. Overall, the proposed framework supports rational CO ₂ electrolysis design and performance‐oriented optimization of integrated CO ₂ R and renewable hydrogen systems.

In this review, the potential role of machine learning in sustainable energy and SGDs is analyzed; energy forecasting, planning, renewable energy production and storage are covered and an extensive perspective on the future role of ML is provided.