Publications & manuscripts.

Graph neural network-based multi-objective Bayesian optimization for enhanced screening of metal–organic frameworks with optimal separation performance

Lane E. Schultz, Nickolas Gantzler, N. Scott Bobbitt, Dorina F. Sava Gallis, and Rémi Dingreville.

Journal of Materials Chemistry A 14(18), 10836–10853

DOI: 10.1039/D5TA09133K

A general approach for determining applicability domain of machine learning models

Lane E. Schultz, Yiqi Wang, Ryan Jacobs, and Dane Morgan.

npj Computational Materials 11(1), 95

DOI: 10.1038/s41524-025-01573-x

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

Lane E. Schultz, Benjamin Afflerbach, Paul M. Voyles, and Dane Morgan.

Journal of Materiomics 11(4), 100964

DOI: 10.1016/j.jmat.2024.100964

Accelerating ensemble uncertainty estimates in supervised materials property regression models

Vidit Agrawal, Shixin Zhang, Lane E. Schultz, and Dane Morgan.

Computational Materials Science 246, 113494

DOI: 10.1016/j.commatsci.2024.113494

Ultrafast Oxygen Conduction in Sillén Oxychlorides

Jun Meng, Md Sariful Sheikh, Lane E. Schultz, William O. Nachlas, Jian Liu, Maciej P. Polak, Ryan Jacobs, and Dane Morgan.

Chemistry of Materials 37(9), 3147–3157

DOI: 10.1021/acs.chemmater.4c03163

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

Ryan Jacobs, Lane E. Schultz, Aristana Scourtas, K. J. Schmidt, Owen Price-Skelly, Will Engler, Ian Foster, Ben Blaiszik, Paul M. Voyles, and Dane Morgan.

Machine Learning: Science and Technology 5(4), 045051

DOI: 10.1088/2632-2153/ad95db

Foundry-ML – Software and Services to Simplify Access to Machine Learning Datasets in Materials Science

K. J. Schmidt, Aristana Scourtas, Logan Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian Foster, and Ben Blaiszik.

Journal of Open Source Software 9(93), 5467

DOI: 10.21105/joss.05467

Molecular dynamic characteristic temperatures for predicting metallic glass forming ability

Lane E. Schultz, Benjamin Afflerbach, Izabela Szlufarska, and Dane Morgan.

Computational Materials Science 201, 110877

DOI: 10.1016/j.commatsci.2021.110877

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz, Janine Spethson, Vanessa Meschke, Elliot Strand, Logan Ward, John H. Perepezko, Dan Thoma, Paul M. Voyles, Izabela Szlufarska, and Dane Morgan.

Chemistry of Materials 34(7), 2945–2954

DOI: 10.1021/acs.chemmater.1c03542

Exploration of characteristic temperature contributions to metallic glass forming ability

Lane E. Schultz, Benjamin Afflerbach, Carter Francis, Paul M. Voyles, Izabela Szlufarska, and Dane Morgan.

Computational Materials Science 196, 110494

DOI: 10.1016/j.commatsci.2021.110494

Molecular simulation-derived features for machine learning predictions of metal glass forming ability

Benjamin T. Afflerbach, Lane Schultz, John H. Perepezko, Paul M. Voyles, Izabela Szlufarska, and Dane Morgan.

Computational Materials Science 199, 110728

DOI: 10.1016/j.commatsci.2021.110728

Microalloying effect in ternary Al-Sm-X (X=Ag, Au, Cu) metallic glasses studied by ab initio molecular dynamics

J. Xi, G. Bokas, L. E. Schultz, M. Gao, L. Zhao, Y. Shen, J. H. Perepezko, D. Morgan, and I. Szlufarska.

Computational Materials Science 185, 109958

DOI: 10.1016/j.commatsci.2020.109958

Quantitative 10–50 kHz X-ray radiography of liquid spray distributions using a rotating-anode tube source

B. R. Halls, J. R. Gord, L. E. Schultz, W. C. Slowman, M. D. A. Lightfoot, S. Roy, and T. R. Meyer.

International Journal of Multiphase Flow 109, 123–130

DOI: 10.1016/j.ijmultiphaseflow.2018.07.014

Accelerating LLZO–Polymer Composite Electrolyte Microstructure Screening Through Multi-Objective Bayesian Optimization

Lane E. Schultz, Scott Q. Monismith, Noah M. Francis, Josey D. McBrayer, and Rémi Dingreville.

Atomistically Resolved Surface Binding Energies for Mo–S Systems: Implications for Sputter Yield and Film Deposition Modeling

Lane E. Schultz, Alex J. Mings, Melissa L. Meyerson, Nathan Jackson, David P. Adams, and Rémi Dingreville.

Kolmogorov-Arnold Networks Applied to Materials Property Prediction

Ryan Jacobs, Lane E. Schultz, and Dane Morgan.

arXiv

DOI: 10.48550/arXiv.2601.20097

Regression with Large Language Models for Materials and Molecular Property Prediction

Ryan Jacobs, Maciej P. Polak, Lane E. Schultz, Hamed Mahdavi, Vasant Honavar, and Dane Morgan.

arXiv · Preprint; revised April 2026

DOI: 10.48550/arXiv.2409.06080

Design of Torsional Test Stand for Micro-Newton Force Detection

Lane E. Schultz, Thomas J. Cogger, Ryan Good, James Schneider, Robert Rothschild, and William Nollet.

2018 Aerodynamic Measurement Technology and Ground Testing Conference

DOI: 10.2514/6.2018-3737

Development of a portable water quality sensor for river monitoring from small rafts

James Schneider, Lane E. Schultz, Sophie Mancha, Eric Hicks, and Ryan N. Smith.

OCEANS 2016 MTS/IEEE Monterey, 1–10

DOI: 10.1109/OCEANS.2016.7761392