1 citations · 2 across the 13 of their papers we have counts for
15 papers
Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning
Can Polat, Mustafa Kurban, Erchin Serpedin +1
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion predicti…
Physics as the label for measuring and correcting materials reasoning in multimodal models
Hasan Kurban, Rasul Khanbayov, Mustafa Kurban
Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate physical law. Evaluation match…
Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second mod…
Grounded verification of chemical and materials reasoning: detection is the bottleneck
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Language models are moving into chemistry and materials discovery workflows, where a wrong molecular formula, space group, or formation energy can silently propagate into downstrea…
The parity gap in crystal tensor prediction
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Crystal symmetry dictates whether a physical response tensor must vanish, establishing a direct test for machine learning predictions independent of property calculations. We deriv…
STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
Can Polat, Erchin Serpedin, Mustafa Kurban +1
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquis…
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials
Can Polat, Erchin Serpedin, Mustafa Kurban +1
interatomic potentials, despite their algebraic elegance, predict force magnitudes accurately but force directions poorly. Across ten rMD17 molecules, every $L \…
How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this the extrapolation frontier. Despite its…
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…
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…