Development of Artificial intelligence-based Interatomic Potentials High-Entropy Diborides for Modelling the Physical and Thermal Properties

The interatomic potentials designed for binary/high entropy diborides, and ultra-high temperature composites (UHTC) have been developed through the implementation of deep neural network (DNN) algorithms. These algorithms employed two different approaches and corresponding codes; 1) strictly local & invariant scalar-based descriptors as implemented in the DEEPMD code and 2) equivariant tensor-based descriptors as included in the ALLEGRO code. The samples for training and validation sets of the forces, energy, and virial data were obtained from the ab-initio molecular dynamics (AIMD) simulations and Density Functional Theory (DFT) calculations including the simulation data from the ultra-high temperature region (> 2000K). We then compared the accuracy of the Deep Learning potentials to predict not only the ground-state properties such as the elastic constants and the phonon dispersion curves but also the ultra-high temperature properties including the lattice parameters and melting behaviors.