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Performance of two complementary machine-learned potentials in modelling chemically complex systems

Engineering and Technology

Performance of two complementary machine-learned potentials in modelling chemically complex systems

K. Gubaev, V. Zaverkin, et al.

This research investigates the performance of two advanced machine-learned potentials—the moment tensor potential and the Gaussian moment neural network—in modeling the Ta-V-Cr-W alloy family, showcasing their abilities to describe complex configurational and vibrational properties with high accuracy. The study was conducted by Konstantin Gubaev, Viktor Zaverkin, Prashanth Srinivasan, Andrew Ian Duff, Johannes Kästner, and Blazej Grabowski.

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