most citedMachine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

3 citations · 3 across the 1 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2026★ 3 cited

Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

Lane E. Schultz, Benjamin Afflerbach, Paul M. Voyles +1

We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties. We compare results for features derived from easy-to-c…

cond-mat.mtrl-sci2026

Kolmogorov-Arnold Networks Applied to Materials Property Prediction

Ryan Jacobs, Lane E. Schultz, Dane Morgan

Kolmogorov-Arnold Networks (KANs) were proposed as an alternative to traditional neural network architectures based on multilayer perceptrons (MLP-NNs). The potential advantages of…