KAYA ERYILMAZ
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Explainable Latent-Space and Anomaly-Sensitive Learning for Thermodynamic Crystal Stability Prediction
Publication

Explainable Latent-Space and Anomaly-Sensitive Learning for Thermodynamic Crystal Stability Prediction

2026
Within the scope of this study, on a large dataset consisting of 10,000 crystal materials; We have developed and tested a multimodal and ex…

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Investigating Tree-Based Models Across Positive–Unlabeled Learning Frameworks for Crystal Synthesizability Prediction
Publication

Investigating Tree-Based Models Across Positive–Unlabeled Learning Frameworks for Crystal Synthesizability Prediction

2026
The majority of crystal structures in materials databases (Materials Project, etc.) are theoretically generated and have not been experimen…

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Hybrid Graph–Machine Learning Framework for Accurate and Interpretable Band Gap Prediction
Publication

Hybrid Graph–Machine Learning Framework for Accurate and Interpretable Band Gap Prediction

2026
Prediction of the electronic band gap, a critical parameter in semiconductor and energy materials, was carried out by combining various mac…

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Tubitak 2209a  - Multi-Channel Image-Based Hybrid Model for Stellar Spectral Classification
Project

Tubitak 2209a - Multi-Channel Image-Based Hybrid Model for Stellar Spectral Classification

2026
A hybrid AI architecture developed using multi-band (U, G, R, I, Z) photometric data and RGB images from the SDSS (Sloan Digital Sky Survey…

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KAYA ERYILMAZ

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