From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

Chandrasekaran Pitchai; Ting‐Yu Lo; Yu‐Ting Hsu; I Chen; Hung‐Chung Li; Chih-Ming Chen

doi:10.1039/d6cc01937d

January 1, 2026Chemical Communications

From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

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Key Points

This research aims to explore how machine learning can improve the discovery of electrocatalysts for water splitting.
Utilized machine learning techniques to analyze electrocatalysts
Assessed effects of data quality on predictions
Investigated thermodynamic bias in catalyst performance
Identified key factors affecting electrocatalyst efficiency
Demonstrated improved prediction accuracy with enhanced data quality
Revealed limitations in current approaches due to thermodynamic biases

Implication

Demonstrates improved electrocatalyst design using machine learning, indicating enhanced efficiency in renewable energy.

Abstract

Machine learning (ML) is emerging as a powerful strategy to accelerate electrocatalyst discovery for water splitting, yet its impact is still limited by data quality, thermodynamic bias, and weak coupling...

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Cite This Study

Pitchai et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc76ddee9eb8c0dce846e https://doi.org/10.1039/d6cc01937d

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From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

Key Points

Implication

Abstract

Discussion

Cite This Study

From Prediction to Materials Design: Machine Learning in Electrocatalytic Water Splitting

Key Points

Implication

Abstract

Discussion

Cite This Study

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