Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials

Atomistic understanding of mechanics and failure of materials is the key for engineering and applications. Modeling accurately brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events for large system sizes. Machine Learned (ML) potentials have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of ML potentials for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is yet uncommon. In this study, we discuss apparent limitations of ML potentials to describe deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture of crystals. We applied the proposed approach to 2D crystal graphene, utilizing the density-functional tight-binding (DFTB) method for more efficient and extensive data generation in place of density functional theory (DFT). Then, we explore how the data selection affects the accuracy of the developed artificial neural network potential (NNP), indicating that only the errors in total energies and atomic forces are insufficient to judge the NNP’s reliability. Therefore, we evaluate and select NNPs based on their performance in describing physical properties, e.g., stress-strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag lattice directions, as well as higher fracture toughness of zigzag edge direction. Our study provides significant insight into crack propagation mechanisms at atomic scales and highlights strategies for NNP developments of broader materials.

In situ transmission electron microscopy observation of the deformation and fracture processes of an epoxy/silica nanocomposite

Herein, we report the in situ transmission electron microscopy observation of the deformation and fracture processes of an epoxy resin thin film containing silica nanoparticles under tensile strain. Under tensile...

A mesoscopic analysis of a localized shear band propagation effect on the deformation and fracture of coated materials

The numerical simulations of the deformation and fracture in an iron boride coating – steel substrate composition are presented. The dynamic boundary-value problem is solved numerically by the finite-difference method. A complex geometry of the borided coating – steel substrate interface is taken into account explicitly. To simulate the mechanical behavior of the steel substrate, use is made of an isotropic strain hardening model including a relation for shear band propagation. Local regions of bulk tension are shown to arise near the interface even under simple uniaxial compression of the composition and in so doing they determine the mesoscale mechanisms of fracture. The interrelation between plastic deformation in the steel substrate and cracking of the borided coating is studied. Stages of shear band front propagation attributable to the interface complex geometry have been revealed. The coating cracking pattern, location of the fracture onset regions and the total crack length are found to depend on the front velocity in the steel substrate.