Abstract The development of β -titanium alloys with bone-mimicking elastic moduli remains a significant challenge. Although machine learning has the potential to accelerate alloy discovery, traditional methods often face data limitations such as sparsity, compositional discontinuity, and feature heterogeneity, leading to overfitting and restricting the exploration of novel compositional spaces. In this study, we introduce a domain-adversarial neural network framework that balances predictive accuracy with the generalization ability of unexplored composition space through integrated feature alignment and adversarial training. Using this approach, we successfully developed a non-intuitive β -Ti alloy with an ultra-low elastic modulus of 28 ± 3 GPa, providing new insights beyond conventionally designed biomedical titanium alloys. This work establishes a screening framework for materials discovery in small-sample data spaces, with broad implications for the design of biomedical and other alloy systems.
Zhou et al. (Thu,) studied this question.