Effective design space exploration of gradient nanostructured materials using active learning based surrogate models

Highlights: • An efficient adaptive material design platform is established for accelerating novel material discovery and design. • Potential of adaptive sampling is demonstrated in balancing the trade-off between fidelity and efficiency in material design. • The collective effects of grain and twin gradients on strength and deformation mechanisms of GNT metals is uncovered. • The importance of creating sufficiently large twin gradients in realizing the full potential of GNT metals is demonstrated. -- Abstract: Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed to develop the surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations.