Vedasri Bai Khavala, Abhijai Velluva, Adhithyan Kathiravan, Harish Kuruva, Chandan Singh Khavala, B. S. Murty, Tiju Thomas
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引用次数: 0
Abstract
Data-driven approaches have the potential to make a priori predictions. However, there are very few models that have been explored for the prediction of nanocomposite electrocatalysts under testing conditions. Here we report for the first time the parametric optimization coupled with data-driven approaches of efficient electrocatalysts for the oxygen evolution reaction (OER) in alkaline media. The parametric optimization suggests that the porous Ni- or Fe-based nitride composites, with combinatorial structures or thin films with average d-electrons between 5 and 8 and catalyst loading between 1 and 10 mg cm−2 will exhibit the best OER activity. Machine learning classification of OER overpotential grades (η10, overpotential at 10 mA cm−2) of transition metal oxynitrides (TMONs) and transition metal oxides (TMOs) is achieved using random forest (RF) and CatBoost algorithms. η10 is classified as grade ‘A’ if η10 < 351 mV, else grade ‘F’. We note that 80–85% of electrocatalysts containing nickel foam (NF) have been in grade A, implying NF is a prospective hindrance against true activity determination of the electrocatalyst but suitable for achieving grade ‘A’ electrocatalyst for electrolysers. RF and CatBoost models achieved an accuracy of 78.09% on the TMON dataset and RF model achieved 72.88% on the TMO dataset. This work aims to reduce the experimental time for the design and development of an electrocatalyst using a data-driven paradigm.
期刊介绍:
Electrocatalysis is cross-disciplinary in nature, and attracts the interest of chemists, physicists, biochemists, surface and materials scientists, and engineers. Electrocatalysis provides the unique international forum solely dedicated to the exchange of novel ideas in electrocatalysis for academic, government, and industrial researchers. Quick publication of new results, concepts, and inventions made involving Electrocatalysis stimulates scientific discoveries and breakthroughs, promotes the scientific and engineering concepts that are critical to the development of novel electrochemical technologies.
Electrocatalysis publishes original submissions in the form of letters, research papers, review articles, book reviews, and educational papers. Letters are preliminary reports that communicate new and important findings. Regular research papers are complete reports of new results, and their analysis and discussion. Review articles critically and constructively examine development in areas of electrocatalysis that are of broad interest and importance. Educational papers discuss important concepts whose understanding is vital to advances in theoretical and experimental aspects of electrochemical reactions.