Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
Eva Zurek,
Josiah Roberts,
Biswas Rijal
et al.
Abstract:Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned
interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor
potentials (MTPs) for four carbide systems: HfTaC, HfZrC, MoWC and TaTiC. The resulting MTPs are used to relax ∼6300 random symmetric
structures, and subsequently improved via active learning to generate robust potentials (RP) that can … Show more
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