976 citations · 1.8k across the 20 of their papers we have counts for
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Dynamic Formation of Preferentially Lattice Oriented, Self Trapped Hydrogen Clusters
M. A. Cusentino, E. L. Sikorski, M. J. McCarthy +2
A series of MD and DFT simulations were performed to investigate hydrogen self-clustering and retention in tungsten. Using a newly develop machine learned interatomic potential, sp…
Exploring Model Complexity in Machine Learned Potentials for Simulated Properties
Andrew Rohskopf, James Goff, Dionysios Sema +5
Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency o…
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…
Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
Sandeep Kumar, Hossein Tahmasbi, Kushal Ramakrishna +5
We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP…
Atomic Representations of Local and Global Chemistry in Complex Alloys
Megan J. McCarthy, Jacob Startt, Rémi Dingreville +2
The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating…