98 citations · 221 across the 7 of their papers we have counts for
7 papers
Describe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters
C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson +5
Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to r…
Effect of helium bubbles on the mobility of edge dislocations in copper
Minh Tam Hoang, Nithin Mathew, Daniel N. Blaschke +1
Helium bubbles can form in materials upon exposure to irradiation. It is well known that the presence of helium bubbles can cause changes in the mechanical behavior of materials. T…
Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Yifeng Tian, Soumendu Bagchi, Liam Myhill +5
Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic def…
Strain Functionals: A Complete and Symmetry-adapted Set of Descriptors to Characterize Atomistic Configurations
Edward M. Kober, Jacob P. Tavenner, Colin M. Adams +1
Extracting relevant information from atomistic simulations relies on a complete and accurate characterization of atomistic configurations. We present a framework for characterizing…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Automated discovery of a robust interatomic potential for aluminum
Justin S. Smith, Benjamin Nebgen, Nithin Mathew +8
Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM…