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Creators/Authors contains: "Schultz, Lane E"

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A general approach for determining applicability domain of machine learning models

https://doi.org/10.1038/s41524-025-01573-x

Schultz, Lane E; Wang, Yiqi; Jacobs, Ryan; Morgan, Dane (December 2025, npj Computational Materials)

Free, publicly-accessible full text available December 1, 2026
Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

https://doi.org/10.1016/j.jmat.2024.100964

Schultz, Lane E; Afflerbach, Benjamin; Voyles, Paul M; Morgan, Dane (July 2025, Journal of Materiomics)

Free, publicly-accessible full text available July 1, 2026
Molecular dynamic characteristic temperatures for predicting metallic glass forming ability

https://doi.org/10.1016/j.commatsci.2021.110877

Schultz, Lane E.; Afflerbach, Benjamin; Szlufarska, Izabela; Morgan, Dane (January 2022, Computational Materials Science)
null (Ed.)
Full Text Available
Exploration of characteristic temperature contributions to metallic glass forming ability

https://doi.org/10.1016/j.commatsci.2021.110494

Schultz, Lane E.; Afflerbach, Benjamin; Francis, Carter; Voyles, Paul M.; Szlufarska, Izabela; Morgan, Dane (August 2021, Computational Materials Science)
null (Ed.)
Full Text Available
Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

https://doi.org/10.1021/acs.chemmater.1c03542

Afflerbach, Benjamin T.; Francis, Carter; Schultz, Lane E.; Spethson, Janine; Meschke, Vanessa; Strand, Elliot; Ward, Logan; Perepezko, John H.; Thoma, Dan; Voyles, Paul M.; et al (April 2022, Chemistry of Materials)

Full Text Available