Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Electrocatalyst and electrolyte research traditionally relies on experimental screening.
• Developing new materials for electrochemical applications is often slow and resource-intensive.
• A data-driven approach is needed to accelerate the discovery of efficient electrocatalysts and electrolytes.

Purpose of the Study:

• To present a comprehensive machine learning (ML) and high-throughput screening (HTS) pipeline for electrocatalyst and electrolyte research.
• To unify catalyst and electrolyte screening at electrochemical interfaces.
• To integrate thermodynamic and kinetic modeling, data realism, and descriptor universality into the ML-HTS workflow.

Main Methods:

• Systematic classification of ML-HTS workflow components, including database construction and descriptor design.
• Categorization of descriptors (geometric, electronic, energetic, integrated) correlated with material properties.
• Enumeration of ML model training and HTS cases for various material classes (single-atom, dual-atom catalysts, high-entropy alloys, electrolytes, additives).

Main Results:

• Demonstration of an end-to-end ML-HTS pipeline applicable to diverse electrochemical materials.
• Identification of key descriptors crucial for predicting activity, selectivity, and stability.
• Successful application of the pipeline to various catalyst and electrolyte systems.

Conclusions:

• Coupling ML-driven HTS with surface and interface understanding significantly accelerates rational electrocatalyst and electrolyte design.
• Addressing challenges in database quality, model transferability, and standardization is crucial for future progress.
• This integrated approach offers a powerful paradigm shift in materials discovery for electrochemical applications.