Dušan Strmčnik

National Institute of Chemistry, SI



Dr. Dušan Strmčnik received his PhD in Chemistry from University of Ljubljana, Slovenia in 2007 and then spent 15 years first as a post-doc and then as a staff scientist at Materials Science Division at Argonne National Laboratory. In 2022, he joined Materials Chemistry Division at National Institute of Chemistry, Ljubljana as a senior research fellow. His research focus is fundamental understanding of electrochemical interfaces in aqueous and non-aqueous environments relevant for energy conversion and storage in fuel cells, electrolyzers and batteries. Dušan currently leads two fundamental projects funded by Slovenian Research Agency ARIS: i) Fundamental Understanding of Hydrogen Evolution Reaction for a New Generation of Nickel-based Electrocatalysts in Alkaline Water and Chlor-alkali Electrolysis; ii) Enhancing the Performance of Energy Conversion and Storage Systems Through 2D Modified Electrochemical Interfaces Dušan has published over 60 articles in scientific journals. He holds a Hirsch’s index h=44 and his papers have been cited over 20,000 times (Google Scholar).


Machine Learning for Investigation of Nickel Surface Chemistry in Electrocatalytic Production of Hydrogen


The increasing energy demand is becoming one of the major global challenges of modern time, causing both economic and existential crises. One of the alternative and clean ways of producing, capturing, and utilizing energy is converting sustainable energy into hydrogen gas, which is an ideal candidate because of its high energy density and zero emission of pollutants during (electro)chemical conversion. Electrochemical production of hydrogen through alkaline water and chlor-alkali electrolysis are two of the more promising approaches that fit into the clean and sustainable energy cycle. Currently, one of the most used metals in industrial-scale electrolytic hydrogen production is nickel.
While Ni has historically been one of the go-to materials for hydrogen evolution production (HER), the lack of fundamental understanding of the interfacial processes on the atomic/molecular level has hindered major progress. Ni has a very rich and complex surface chemistry, making the analysis and comparison of individual samples challenging and the fundamental insights extremely elusive.
We will present the utilization of hierarchical agglomerative clustering methods and symbolic regression methods to extract meaningful insights from large datasets generated through advanced analytical techniques such as TOF-SIMS, XPS and electrochemical methods on Ni-based materials and identify the relationships between surface characteristics and material (electro)chemical properties.
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