13 papers
A single design choice determines whether machine learning models of materials make physically impossible predictions
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how m…
When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the --atom regime; combining t…
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…
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…