Machine learning for improved fuel cell catalysts
Our work is presented in a new press article by the "e-conversion" Cluster of Excellence
Platinum is the common catalyst material for the oxygen reduction in fuel cells and its main cost factor.
Using machine learning, we now can forecast and optimize the performance of new core-shell catalysts.
Our proposed catalysts have high oxygen reduction activities, which can significantly reduce the amount of platinum.
Publication:
Oxygen Reduction Activities of Strained Platinum Core–Shell Electrocatalysts Predicted by Machine Learning.
Marlon Rück, Batyr Garlyyev, Felix Mayr, Aliaksandr S. Bandarenka, and Alessio Gagliardi.
The Journal of Physical Chemistry Letters 2020 11 (5), 1773-1780.
DOI:10.1021/acs.jpclett.0c00214.
https://pubs.acs.org/doi/10.1021/acs.jpclett.0c00214
https://www.e-conversion.de/machine-learning-for-improved-fuel-cell-catalysts/