Comparison of derivative-free and gradient-based minimization for multi-objective compositional design of shape memory alloys

Designing shape memory alloys (SMAs) that meet performance targets while remaining affordable and sustainable is a complex challenge. In this work, we focus on optimizing SMA compositions to achieve an arbitrary target martensitic start temperature (Ms) while minimizing cost. To do this, we use machine learning models as surrogate predictors and apply numerical optimization methods to search for suitable alloy combinations. We trained two types of machine learning models, a tree-based ensemble and a neural network, using a dataset of experimentally characterized alloys and physics-informed features. The tree-based model was used with a derivative-free optimizer (COBYLA), while the neural network, which provides gradient information, was paired with a gradient-based optimizer (TRUST-CONSTR). Our results show that while both models predict Ms with similar accuracy, the optimizer paired with the neural network finds better solutions more consistently. COBYLA often converged to suboptimal results, especially when the starting guess was far from the target. The TRUST-CONSTR method showed more stable behavior and was better at reaching alloy compositions that met both objectives. This study demonstrates a practical approach to exploring new SMA compositions by combining physics-informed data, machine learning models, and optimization algorithms. Although the scale of our dataset is smaller than simulation-based efforts, the use of experimental data improves the reliability of the predictions. The approach can be extended to other materials where design trade-offs must be made with limited data. Josyula et al. show that while a tree-based ensemble can provide slightly better accuracy than a neural network in predicting phase transformation temperatures in shape memory alloys from the elemental compositions, a gradient-based optimizer paired with the neural network finds better solutions more consistently than a derivative-free optimizer, and is therefore more useful for alloy discovery and design. • A multi-objective optimization approach is applied to shape memory alloy compositions. • Cost and transformation temperatures are simultaneously considered. • Neural networks enable gradient-based objective function minimization.