Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB2

Abstract

Transition metal diboride (TMB₂) ceramics combine high hardness with excellent thermal and chemical stability, making them attractive for protective coating applications. TMB₂s commonly grow as largely off-stoichiometric—containing vacancies or other simple crystallographic defects—and a particularly essential question is how such defects alter the response to mechanical loads at the nanoscale. We exploit molecular dynamics (MD) equipped with here-trained machine-learning interatomic potential (MLIP) to reveal effects of point and planar defects during nano-mechanical tests of TiB₂, being a representative of the most common AlB₂-type TMB₂s. MLIP training consists of active learning on configurations from finite temperature ab initio molecular dynamics, including equilibrium and uniaxially loaded structures as well as various defective and/or extremely strained environments. Transferability to near-indenter-tip environments is achieved via on-the-fly training on extrapolative clusters from simple nanoindentation runs. Following MLIP's validation, we simulate room-temperature nanoindentation of TiB_2−x structures, where B sub-stoichiommetry is realized by disordered B vacancies, single- and double-planar defects previously revealed by electron microscopy. A somewhat non-intuitive prediction is that TiB_2−x structures can exhibit hardness comparable to the ideal TiB₂, challenging traditional assumptions about weakening effects of sub-stoichiometry. This behavior is ascribed to Ti-rich planar defects, contrarily to vacancies which, at the same chemistry, notably deteriorate mechanical properties. We hypothesize that similar effects may be expected for other group 5–6 transition metal diborides.