Recent advances in the data-driven development of emerging electrocatalysts

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Recent advances in the data-driven development of emerging electrocatalysts
Title:
Recent advances in the data-driven development of emerging electrocatalysts
Journal Title:
Current Opinion in Electrochemistry
Publication Date:
04 October 2023
Citation:
Ding, K., Yang, T., Leung, M. T., Yang, K., Cheng, H., Zeng, M., Li, B., & Yang, M. (2023). Recent advances in the data-driven development of emerging electrocatalysts. Current Opinion in Electrochemistry, 42, 101404. https://doi.org/10.1016/j.coelec.2023.101404
Abstract:
Data-driven strategies have proven efficient for the design of high-performance electrocatalysts from the vast material search space. In this review, we present an overview on data-driven approaches to emerging electrocatalyst design: high-throughput experiments, high-throughput calculations, and machine learning. High-throughput experiments facilitate rapid synthesis and characterization of electrocatalysts, leading to efficient exploration of various materials. High-throughput calculations predict and screen materials’ properties, allowing for the identification of promising electrocatalysts. The integration of machine learning further augments these high-throughput approaches through critical insight extracted from the large dataset, fast prediction of materials’ performance, and optimization of materials discovery. Employing these data-driven strategies synergistically could accelerate the development of electrocatalysts. Such advancements could promote green energy technologies and substantially contribute to mitigating grand challenges posed by global climate change.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
acknowledges the funding support from The Hong Kong Polytechnic University (project number: 1-BE47, ZE0C, ZE2F and ZE2X).
Description:
ISSN:
2451-9103
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