most citedUnderstanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding

1 citations · 2 across the 13 of their papers we have counts for

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cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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…

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…