Gün Deniz Akkoç

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A fast transition from fossil fuels to renewable energy is now a must and sun has the greatest potential for this transition. Yet, not only harvesting but also the storage of the energy is just as important challenge. Ideally, a photoelectrochemical water splitting catalyst would offer solutions to both of these challenges. Alternatively, a conventional water splitting catalyst can still be used in combination with other renewable energy sources. These catalysts, however, must meet a very large set of standards such as high stability, sufficient performance and low cost. The lack of a complete theoretical approach for evaluation of catalyst candidates makes a high-throughput approach a viable option. Yet even with a very rapid high-throughput technique, the complete screening of the entire material space is far from being feasible. This is where the machine learning can play a big part to reverse-engineer this problem and hasten the discovery of ideal (photo)electrocatalyst.

 

In my research, I mainly focus on an iterative approach where a rapid synthesis is carried out with an inkjet printer or with drop-casting, followed by (photo)electrochemical evaluation of the catalysts through Scanning Flow Cell (SCF) and Scanning Droplet Cell, and finally creating/updating a machine learning model to move towards a more optimal candidate.

 


2020–present Data scientist and head of digitalization for machine learning guided synthesis of custom cathode materials for lithium-ion batteries.
2019–present Izmir Institute of Technology, Department of Chemistry, PhD, "Machine Learning Assisted Combinational High Throughput Approach for Discovery of Multi-Metal Oxide Water Splitting Catalysts"
2016–present Software developer for machine learning assisted quantitative modeling of chemical properties of hydrocarbons with spectroscopic data
2016–2019 Izmir Institute of Technology, Department of Chemistry, MSc, "Development of Chemometric Calibration Toolbox and Its Application for Determination of Salep Adulteration"
2016–2017 Researcher for project "Development of non-invasive method for early diagnosis of lung cancer based on analyses of exhaled breath"
2010–2016 Izmir Institute of Technology, Department of Chemistry, BSc


2016 Third International Conference on New Trends in Chemometrics and Applications, in Antalya, Turkey Second best poster award: “Quantitative Determination of Mixed Fruit Juice Composition Using a Smart Phone and Multivariate Calibration”