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We predict the critical resolved shear stress (CRSS) for slip for all the studied configurations encompassing random and SRO structures and discuss the effect of SRO on the flow stress. The proposed ML (machine learning) methodology provides atomic ar- rangements from target order parameters with high accuracy, thereby eliminating the need for expensive sim- ulations, and it advances the understanding of SRO at the atomistic scale. 

Critical Resolved Shear Stress (CRSS) determination is the focus of this paper. 

Abstract This article introduces a computational design framework for obtaining three‐dimensional (3D) periodic elastoplastic architected materials with enhanced performance, subject to uniaxial or shear strain. A nonlinear finite element model accounting for plastic deformation is developed, where a Lagrange multiplier approach is utilized to impose periodicity constraints. The analysis assumes that the material obeys a von Mises plasticity model with linear isotropic hardening. The finite element model is combined with a corresponding path‐dependent adjoint sensitivity formulation, which is derived analytically. The optimization problem is parametrized using the solid isotropic material penalization method. Designs are optimized for either end compliance or toughness for a given prescribed displacement. Such a framework results in producing materials with enhanced performance through much better utilization of an elastoplastic material. Several 3D examples are used to demonstrate the effectiveness of the mathematical framework.