78 citations · 142 across the 7 of their papers we have counts for
7 papers
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…
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…
The Materials Simulation Toolkit for Machine Learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials research
Ryan Jacobs, Tam Mayeshiba, Ben Afflerbach +5
As data science and machine learning methods are taking on an increasingly important role in the materials research community, there is a need for the development of machine learni…
Exploring effective charge in electromigration using machine learning
Yu-chen Liu, Benjamin Afflerbach, Ryan Jacobs +2
The effective charge of an element is a parameter characterizing the electromgration effect, which can determine the reliability of interconnection in electronic technologies. In t…