24 citations · 24 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2023★ 24 cited
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…
cond-mat.mtrl-sci2021
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…