24 citations · 44 across the 5 of their papers we have counts for
5 papers
Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features
Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz +9
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model wa…
Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
Lane E. Schultz, Benjamin Afflerbach, Izabela Szlufarska +1
We explore the use of characteristic temperatures derived from molecular dynamics to predict aspects of metallic Glass Forming Ability (GFA). Temperatures derived from cooling curv…
Molecular simulation-derived features for machine learning predictions of metal glass forming ability
Benjamin T. Afflerbach, Lane Schultz, John H. Perepezko +3
We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. In this work we sh…
Exploration of Characteristic Temperature Contributions to Metallic Glass Forming Ability
Lane E. Schultz, Benjamin Afflerbach, Carter Francis +3
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predicti…
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 +6
The icosahedral-like polyhedral fraction (ICO-like fraction) has been studied as a criterion for predicting the glass-forming ability of bulk ternary metallic glasses, Al90Sm8X2 (X…