1.8k citations · 2.1k across the 8 of their papers we have counts for
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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…
Rapid Production of Accurate Embedded-Atom Method Potentials for Metal Alloys
Elan J. Weiss, Logan Ward, Christian Oberdorfer +4
A critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the limited availability of suitable interatomic potentials. Here, we introduce the…
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
Logan Ward, Ankit Agrawal, Alok Choudhary +1
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior example…