1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.MA2025★ 1 cited
xChemAgents: Agentic AI for Explainable Quantum Chemistry
Can Polat, Mehmet Tuncel, Mustafa Kurban +2
Recent progress in multimodal graph neural networks has demonstrated that augmenting atomic XYZ geometries with textual chemical descriptors can enhance predictive accuracy across…
cs.CV2025
Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning
Can Polat, Hasan Kurban, Erchin Serpedin +1
Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a mu…
cs.LG2025★ 1 cited
Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat, Hasan Kurban, Erchin Serpedin +1
Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like…