An efficient GPU-accelerated calibration of crystal plasticity model parameters by multi-objective optimization with automatic differentiation-based sensitivities

Accurate and efficient determination of crystal plasticity (CP) material parameters is essential for predictive simulations that link microstructures, manufacturing processes, and material properties. This study presents a GPU-accelerated pipeline for calibrating CP material parameters, integrating automatic differentiation (AD)-based sensitivities with gradient-based optimization, built upon our open-source JAX-CPFEM package. This method eliminates reliance on finite-differences in gradient-based approaches while improving efficiency over gradient-free optimization. The effectiveness of the pipeline is demonstrated through five case studies covering various crystal structures and boundary conditions. First, the AD-based sensitivity analysis achieves over 10× speedup compared to finite-difference while maintaining accuracy for complex, nonlinear constitutive laws. Second, a comprehensive analysis of initial starting points on gradient-based optimization demonstrates that using appropriate bounds mitigates potential issues. Across both single-crystal and polycrystalline cases calibrating six material parameters, our pipeline requires approximately 7× fewer iterations and achieves 3× higher efficiency over popular gradient-free methods like Bayesian optimization, regardless of geometry complexity. Furthermore, the successful calibration of twelve parameters in a dual-phase steel model highlights the capability of pipeline to handle high-dimensional optimization problems, which is challenging for gradient-free optimization. Finally, the robustness of our pipeline is validated using noisy synthetic data and experimental tensile data for wrought IN625 over a finite strain range. These results illustrate the applicability of our pipeline to real-world scenarios and its potential for high-dimensional optimization and promising applications in Integrated Computational Materials Engineering workflows.