Search for: All records

Abstract This study investigates the grain boundary energy dependence on segregated dopants in nanocrystalline zinc aluminate ceramics. Atomistic simulations of Σ3 and Σ9 grain boundaries showed that trivalent ions of varying ionic radii [Sc³⁺(74.5 pm), In³⁺(80.0 pm), Y³⁺(90.0 pm), and Nd³⁺(98.3 pm)] have a tendency to segregate to both interfaces, with Y³⁺presenting the highest segregation potentials. The connection between segregation and the reduction of interfacial energies was explored by measuring the grain boundary energy on nanoceramics fabricated via high‐pressure spark plasma sintering (HP‐SPS) using differential scanning calorimetry (DSC). The results revealed that Y³⁺doping at 0.5 mol% reduces the grain boundary energy in zinc aluminate nanoceramics from 1.1–1.3 J/m²to 0.6–0.8 J/m²; the range correlates with the observed size dependence of the excess energy, with higher values observed for the smaller grain sizes (∼17 nm). The noted decrease in interfacial energies for doped samples suggests it is indeed possible to alter the stability of zinc aluminate grain boundaries via dopant segregation. 

Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of electronic structure methods with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the description of local atomic environments by some set of descriptors. These should ideally be invariant to the symmetries of the physical system, twice-differentiable with respect to atomic positions (including when an atom leaves the environment), and complete to allow the atomic environment to be reconstructed up to symmetry. The stronger condition of optimal completeness requires that the condition for completeness be satisfied with the minimum possible number of descriptors. Evidence is provided that an updated version of the recently proposed Spherical Bessel (SB) descriptors satisfies the first two properties and a necessary condition for optimal completeness. The Smooth Overlap of Atomic Position (SOAP) descriptors and the Zernike descriptors are natural counterparts of the SB descriptors and are included for comparison. The standard construction of the SOAP descriptors is shown to not satisfy the condition for optimal completeness and, moreover, is found to be an order of magnitude slower to compute than that of the SB descriptors.