Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics

Abstract

Abstract Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects—governing plasticity and crack nucleation in most materials—are too large to be included in the training set. Using TiB2 as a model ceramic material, we propose a training strategy for MLIPs suitable to simulate mechanical response of monocrystals until failure. Our MLIP accurately reproduces ab initio stresses and fracture mechanisms during room-temperature uniaxial tensile deformation of TiB2 at the atomic scale ( ≈ 103 atoms). More realistic tensile tests (low strain rate, Poisson’s contraction) at the nanoscale ( ≈ 104–106 atoms) require MLIP up-fitting, i.e., learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. As our MLIP is specifically trained to modelling tensile deformation, we discuss its limitations for description of different loading conditions and lattice structures with various Ti/B stoichiometries. Finally, we show that our MLIP training procedure is applicable to diverse ceramic systems. This is demonstrated by developing MLIPs which are subsequently validated by simulations of uniaxial strain and fracture in TaB2, WB2, ReB2, TiN, and Ti2AlB2.