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Quantifying the performance of machine learning models in materials discovery
Christopher K. H. Borg, Eric S. Muckley, Clara Nyby +4
The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or…
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…
Mapping Thermoelectric Transport in a Multicomponent Alloy Space
Ramya Gurunathan, Suchismita Sarker, Christopher K. H. Borg +4
Interest in high entropy alloy thermoelectric materials is predicated on achieving ultralow lattice thermal conductivity through large compositional disorder. However, h…