Machine-Learning-Based Model of Elastic-Plastic Deformation of Copper for Application to Shock Wave Problem

Molecular dynamics (MD) simulations explored the deformation behavior of copper single crystal under various axisymmetric loading paths. The obtained MD dataset was used for the development of a machine-learning-based model of elastic-plastic deformation of copper. Artificial neural networks (ANNs) approximated the elastic stress-strain relation in the form of tensor equation of state, as well as the thresholds of homogeneous nucleation of dislocations, phase transition and the beginning of spall fracture. The plastic part of the MD curves was used to calibrate the dislocation plasticity model by means of the probabilistic Bayesian algorithm. The developed constitutive model of elastic-plastic behavior can be applied to simulate the shock waves in thin copper samples under dynamic impact.

Automated summary

In this study, molecular dynamics simulations were used to explore the deformation behavior of copper single crystal under different loading paths, and the obtained data was utilized to develop a machine-learning-based model for copper's elastic-plastic deformation. Artificial neural networks approximated the elastic stress-strain relation, as well as the thresholds for homogeneous nucleation of dislocations, phase transition, and the initiation of spall fracture. The plastic portion of the MD curves was used to calibrate the dislocation plasticity model, enabling the application of the developed constitutive model to simulate shock waves in thin copper samples under dynamic impact.