70 citations · 99 across the 8 of their papers we have counts for
5 papers · 1 filter
Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors
Brad L. Boyce, Mitchell A. Wood, Krishna Garikipati +16
Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechani…
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…