1 citations · 2 across the 6 of their papers we have counts for
7 papers
SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We intr…
C2NP: A Benchmark for Learning Scale-Dependent Geometric Invariances in 3D Materials Generation
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remain…
QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Despite rapid advances in molecular and materials machine learning, most models still lack physical transferability: they fit correlations across whole molecules or crystals rather…
Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These…
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…
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…