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Combination of Rapid Intrinsic Activity Measurements and Machine Learning as a Screening Approach for Multicomponent

Chen Liu^1,2, Yan Ding³, Yanxue Guan^1,2

¹State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Changchun 130000, China.

ACS Applied Materials & Interfaces

|August 30, 2023

Summary

This summary is machine-generated.

This study introduces a rapid method for creating and testing multicomponent catalysts, accelerating the design of new materials. Machine learning analysis revealed that catalyst entropy directly correlates with catalytic activity, guiding future catalyst development.

Keywords:

carbon nanoelectrode high-entropy alloy instinct electrocatalytic activity machine learning multicomponent electrocatalysts

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Area of Science:

Materials Science
Electrochemistry
Computational Chemistry

Background:

Machine learning (ML) and quantum chemistry accurately predict catalyst properties.
Designing multicomponent catalysts with ML is limited by the slow acquisition of experimental data.

Purpose of the Study:

To develop a rapid screening strategy for multicomponent catalyst design using experimental data.
To establish a closed-loop system for catalyst synthesis, characterization, and ML-driven design.
To identify key parameters influencing catalyst activity through ML analysis.

Main Methods:

Nanodroplet-mediated electrodeposition on a carbon nanocorn electrode for rapid catalyst synthesis.
Operando characterization to study catalyst reconstruction during the oxygen evolution reaction.
Development of artificial neural networks for ML analysis of experimental data.

Main Results:

Complete experimental data for AI training was collected in one week.
ML analysis showed a direct proportionality between catalyst entropy and catalytic activity.
High-entropy multicomponent catalysts exhibited greater catalytic activity than medium-low-entropy systems.

Conclusions:

The developed rapid screening strategy accelerates the experimental data acquisition for ML-based catalyst design.
Catalyst entropy is a critical factor for optimizing catalytic activity in multicomponent systems.
This approach provides a guideline for the rational design of efficient catalysts.