Atomic modelling using density-functional theory and machine-learned interatomic potentials, and process optimization through active learning

In a context of climate crisis, hydrogen technology emerges as a key factor, offering a clean fuel with a high energy density which can be produced through water electrolysis. However, the widespread usage of hydrogen as an energy vector is hindered by a significant bottleneck: most electrodes contain platinum group metals (PGMs) due to their high catalytic activity and stability. Such PGMs are scarce and costly and prevent the use of hydrogen and fuel cells on a larger scale. In this context, the NICKEFFECT project aims to develop novel (i) Ni-based coating materials to replace PGMs and ensure high efficiency in key applications such as fuel cells, and (ii) Ni-based ferromagnetic systems for random-access memory applications.

In this work, we present how active learning has been successfully applied to reach optimal catalytic performances of Ni-W dense coatings by tuning the deposition parameters [1]. Additionally, we show how density-functional theory and machine-learned force fields have been used to provide a better understanding of the magneto-ionic properties of Ni-Co oxide patterned microdisks [2].

This work is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.

[1] R. de Paz-Castany et al., ChemSusChem 18, e202400444 (2025)

[2] A. Arredondo-López et al., ACS Applied Materials & Interfaces 17, 9500-9513 (2025)