On the way toward a sustainable energy economy, major challenges still need to be resolved. On the one hand, technologies beyond lithium-ion batteries are required in order to replace scarce lithium in electrolyte and electrode materials. On the other hand, sustainable catalysts for electrocatalytic reactions, such as oxygen or hydrogen evolution and reduction, need to be identified and developed. Material screening is a promising strategy to recognize potential electrode and electrolyte compositions.
The introduction of the computational hydrogen electrode (CHE) led to a renaissance in theoretical electrocatalysis. This approach facilitates conducting conventional Volcano analyses based on binding energies, thus establishing a simple and straightforward tool to screen electrocatalysts. Meanwhile, the Volcano concept has reached the field of battery science to scrutinize electrode and electrolyte compositions.
However, the simple Volcano analysis is not free of uncertainties and errors, which is mainly traced to the fact that the entire analysis relies on thermodynamic considerations only, whereas the kinetics as well as the effect of the applied overpotential on the energetics is omitted. Here, advanced approaches enclosing overpotential, kinetics, or machine-learning techniques in the analysis are called for, enabling material screening beyond a conventional Volcano scheme. The utilization of such extended frameworks may be beneficial to account for a thorough sorting of electrode materials and electrode compositions, before the most promising configurations are further optimized toward developing catalysts with an enhanced electrochemical performance.
This Research Topic is dedicated to Original Research articles, Perspectives, and Review articles that report improved material-screening techniques to identify electrode or electrolyte compositions with applications in electrocatalysis or batteries. In the recent literature combined experiment-theory approaches are emerging so that this Research Topic does not entirely address theoretical work, but combined experiment-theory approaches are also welcome. Themes of interest may include, but are not limited to, the following:
• Material-screening approaches to determine potential electrocatalysts
• Material-screening approaches to determine potential electrode materials for batteries
• Material-screening approaches to determine potential electrolyte compositions for electrocatalytic processes
• Material-screening approaches to determine potential electrolyte compositions for batteries
• Material-screening approaches including kinetics and applied overpotential into the analysis
• Material-screening approaches based on a combination of experiment and theory
• Material-screening approaches comprising machine-learning techniques
On the way toward a sustainable energy economy, major challenges still need to be resolved. On the one hand, technologies beyond lithium-ion batteries are required in order to replace scarce lithium in electrolyte and electrode materials. On the other hand, sustainable catalysts for electrocatalytic reactions, such as oxygen or hydrogen evolution and reduction, need to be identified and developed. Material screening is a promising strategy to recognize potential electrode and electrolyte compositions.
The introduction of the computational hydrogen electrode (CHE) led to a renaissance in theoretical electrocatalysis. This approach facilitates conducting conventional Volcano analyses based on binding energies, thus establishing a simple and straightforward tool to screen electrocatalysts. Meanwhile, the Volcano concept has reached the field of battery science to scrutinize electrode and electrolyte compositions.
However, the simple Volcano analysis is not free of uncertainties and errors, which is mainly traced to the fact that the entire analysis relies on thermodynamic considerations only, whereas the kinetics as well as the effect of the applied overpotential on the energetics is omitted. Here, advanced approaches enclosing overpotential, kinetics, or machine-learning techniques in the analysis are called for, enabling material screening beyond a conventional Volcano scheme. The utilization of such extended frameworks may be beneficial to account for a thorough sorting of electrode materials and electrode compositions, before the most promising configurations are further optimized toward developing catalysts with an enhanced electrochemical performance.
This Research Topic is dedicated to Original Research articles, Perspectives, and Review articles that report improved material-screening techniques to identify electrode or electrolyte compositions with applications in electrocatalysis or batteries. In the recent literature combined experiment-theory approaches are emerging so that this Research Topic does not entirely address theoretical work, but combined experiment-theory approaches are also welcome. Themes of interest may include, but are not limited to, the following:
• Material-screening approaches to determine potential electrocatalysts
• Material-screening approaches to determine potential electrode materials for batteries
• Material-screening approaches to determine potential electrolyte compositions for electrocatalytic processes
• Material-screening approaches to determine potential electrolyte compositions for batteries
• Material-screening approaches including kinetics and applied overpotential into the analysis
• Material-screening approaches based on a combination of experiment and theory
• Material-screening approaches comprising machine-learning techniques