Hakkımızda

The Computational Science and Machine Learning Laboratory (CSML) is an interdisciplinary research group founded by Prof. Dr. Övgü C. Yelgel within the Department of Electrical and Electronics Engineering at Recep Tayyip Erdoğan University.

The group conducts in-depth theoretical and computational investigations of low-dimensional systems, with a particular focus on the electronic, optical, and photocatalytic properties of emerging materials. Using density functional theory (DFT)-based simulations, CSML explores two-dimensional materials for energy-related applications, including photocatalytic water splitting and next-generation catalyst design. Current research directions include the engineering of oxygen vacancies, defect-driven property modulation, and the theoretical development of high-performance materials for sustainable energy conversion.

Building upon this strong physics-based computational background, CSML has progressively expanded its research vision toward the integration of artificial intelligence and machine learning methods with materials modeling and energy system analysis. This transition naturally aligns with the academic profile of the Department of Electrical and Electronics Engineering, where graduate students contribute to research at the intersection of data-driven modeling, renewable energy technologies, and intelligent prediction systems. In this context, CSML actively supports master’s students working on machine learning applications for renewable energy forecasting, performance estimation, optimization, and decision-support frameworks for sustainable energy systems.

By combining computational materials science, machine learning, and electrical-electronics engineering perspectives, CSML aims to accelerate scientific discovery and contribute to the development of efficient, reliable, and environmentally sustainable energy technologies. The laboratory provides a dynamic research environment for early-career researchers and graduate students, while maintaining strong national and international collaborations that foster interdisciplinary scientific growth.