Advances in computational approaches for bridging theory and experiments in electrocatalyst design

Area of Science:

• Electrocatalysis
• Materials Science
• Computational Chemistry

Background:

• Activating inert molecules (CO2, N2, O2) via electrocatalysis is crucial for energy and environmental solutions.
• Catalyst design is hindered by molecular stability and complex interfacial phenomena.
• Bridging theoretical models and experimental realities requires advanced computational approaches.

Purpose of the Study:

• To review computational advances in electrocatalyst design for inert molecule activation.
• To highlight methods for rapid identification and prediction of catalyst performance.
• To discuss the integration of computational and experimental strategies.

Main Methods:

• Scaling relations and descriptor-based screening (Sabatier principle, volcano plots).
• Thermodynamic and kinetic models (computational hydrogen electrode, constant electrode potential, ab initio thermodynamics).
• Advanced simulation techniques (constant potential, explicit solvation, ab initio molecular dynamics, ML-accelerated MD).
• High-throughput workflows and machine learning for exploring materials and pathways.

Main Results:

• Computational methods enable rapid screening and identification of promising electrocatalytic materials.
• Accurate prediction of reaction energetics and catalyst stability under realistic conditions is now possible.
• Understanding of dynamic electrochemical interfaces has significantly improved.
• Streamlined catalyst discovery through data-driven approaches and exploration of vast material spaces.

Conclusions:

• Computational advances provide mechanistic insights into inert molecule activation.
• These methods offer a robust platform for guiding experimental electrocatalyst design.
• Further integration of computational and experimental approaches is key for next-generation electrocatalysts.