Topic: Machine Learning-enabled Exploration of Electrocatalysts for Energy Conversion Technology

This Special Issue of Journal of Materials Informatics, titled "Machine Learning-Enabled Exploration of Electrocatalysts for Energy Conversion Technology," showcases the pivotal role of machine learning in advancing electrocatalysis. By leveraging machine learning, the development and optimization of electrocatalysts critical for sustainable energy technologies have been revolutionized.

The articles featured in this issue cover a wide spectrum, from designing algorithms tailored to materials science to applying these methods in discovering and optimizing novel electrocatalytic systems. Contributors explore how machine learning facilitates the prediction of catalytic performance, identification of new electrocatalyst materials, and refinement of operational parameters, thereby streamlining experimental procedures and accelerating innovation.

This collection not only underscores the impactful integration of machine learning in electrocatalyst research but also outlines the future potential of these technologies to further revolutionize energy conversion efficiency and sustainability. Readers are invited to delve into these contributions to grasp the current trends and explore the future possibilities of machine learning in electrocatalysis.

Machine learning, algorithm development, electrocatalysis, energy conversion, sustainable technology

15 Jun 2025