Onur Caylak

PhD

I pursued my initial academic interests at VU University Amsterdam, where I completed a Bachelor of Science in Mathematics in 2015. Under the guidance of Prof. Dr. Andre Ran.

Moving to Eindhoven for further studies, I enrolled in a Master’s program in Industrial and Applied Mathematics. In 2017, as a part of the master’s programm, I wrote a thesis supervised by Dr. Georg Prokert.

Later in 2017, I initiated his doctoral research, sponsored by the NWO, at the Centre for Analysis, Scientific Computing, and Applications in Eindhoven. Under the guidance and support of Dr. Bjoern Baumeier. My current research primarily revolves around integrating machine learning techniques into electronic structure calculations.

Publications

Machine Learning of Quasiparticle Energies in Molecules and Clusters

Excited-State Geometry Optimization of Small Molecules with Many-Body Green’s Functions Theory

A Kernel based Machine Learning Approach to Computing Quasiparticle Energies within Many-Body Green’s Functions Theory

Glassy dynamics from generalized mode-coupling theory: existence and uniqueness of solutions for hierarchically coupled integro-differential equations

Wasserstein metric for improved quantum machine learning with adjacency matrix representations

Excited-state electronic structure of molecules using many-body Green’s functions: Quasiparticles and electron–hole excitations with VOTCA-XTP

Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials