Nejc Hodnik

National Institute of Chemistry, SI



Assoc. Prof. Dr. Nejc Hodnik leads the Laboratory for Electrocatalysis (>30 researchers) at the National Institute of Chemistry (NIC) and is a PI of the ERC StG. He was also leading projects like NATO SPS on the topic of graphene-based electrocatalysts, ERA-MIN 2 PGM recycling project, and a few other national projects. He is currently supervising two industrial PhD projects underway in his laboratory at NIC, funded by Johnson Matthey and EKPO. These projects are focused on the evaluation and testing of PEMFC and PEM electrolysis catalysts. He earned his PhD at the University of Ljubljana in 2013, followed by a Marie-Curie IEF Scholarship at the Max-Planck Institute in Düsseldorf, Germany. In 2016, he returned to Slovenia and the NIC. Shortly after, he was elected to associate professor at the University of Nova Gorica. He has published over 100 scientific papers, has an h-index of 34, holds three patents and two patent applications, and was/is a mentor of over 10 PhD students.


Exploring Big Data for a Deeper Understanding of Electrocatalyst Behavior


Electrocatalysis is experiencing a growing trend each year, presenting a promising shift for the chemical industry to rely solely on electrical energy sourced from sustainable options like solar and wind power. Through electrochemical energy conversion, we can efficiently store this green energy in chemical bonds. However, the study of electrocatalysts is challenging, requiring numerous trial-and-error tests to understand structure-activity and -stability behaviors. Electrochemical methods are ideal for automation, facilitating a transition to high-throughput techniques. Integration with automated characterization techniques such as electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy results in a substantial generation of data.

The systematic acquisition, storage, processing, and analysis of this data becomes pivotal. I firmly believe that a wealth of untapped insights into electrocatalytic behavior await discovery through the application of machine learning approaches. In my talk, I will showcase a few examples illustrating how these approaches can be exploited. Such strategies have the potential to revolutionize our comprehension, exploration, and enhancement of electrocatalysis, thereby paving the way for new horizons in sustainable energy development.
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