Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning

Abstract

Data-driven modelling, such as machine learning (ML), has great potential to streamline the complexity involved in designing new alloys. However, such powerful predictive models require a high-quality dataset, which is limited in the alloy field due to selection and reporting biases. These biases lead to out-of-distribution (OOD) regions where ML models suffer from predictive performance degradation, limiting the design of innovative alloys. To overcome this problem, we propose a ML-based design strategy for unique and high-performance aluminium (Al) alloys, incorporating multi-objective genetic algorithm (MOGA) and active learning. To guide active learning, the cosine similarity metric embedded MOGA pinpoints unique and high-strength Al alloys in OOD regions for experiments. Our study demonstrates the deficiency of initial ML models when trained on the biased dataset and subsequent improvement in retrained models after applying active learning with alloys suggested by MOGA. On this basis, a new Al alloy that is distinct from the existing dataset is developed with a yield tensile strength of 688 MPa, ultimate tensile strength of 738 MPa, and elongation of 7.5 %. This finding highlights the importance of the inclusion of OOD results and the efficiency of ML in alloy design.