2 citations · 2 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2026
Finite-temperature bulk moduli from an EOS-based Grüneisen function
Çetin Kılıç
An equation-of-state (EOS)-based construction of the Grüneisen function is developed and assessed through predictions of finite-temperature bulk moduli. In this approach, the volum…
cond-mat.mtrl-sci2024★ 2 cited
Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides
Çetin Kılıç, Sümeyra Güler-Kılıç
Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their com…