Iterative multi-objective design of hydrogen embrittlement resistant high-strength steels using Bayesian optimization

The infiltration of hydrogen can weaken the strength and toughness of materials, especially in high-strength steel. Considering the inherent trade-off between strength and toughness in steel design, balancing these two factors with the material's hydrogen embrittlement sensitivity has become a huge challenge in steel design. The long cycle nature of hydrogen embrittlement testing makes the iteration of traditional trial and error methods infeasible for steel development. This work adopts active learning algorithms based on Bayesian and Pareto optimization to iteratively optimize the chemical composition and processing technology of high-strength steel, in order to simultaneously improve its hydrogen filled tensile strength (UTS_H) and hydrogen filled total elongation (TEL_H). After two rounds of iterative optimization, the iterative optimization algorithm significantly improved the strength prediction of materials after hydrogen charging. The machine learning model found 16 types of steel from over 15 million unknown materials. Eight materials showed better comprehensive performance than known data, with one material having a tensile strength exceeding 1800 MPa and an elongation of 12.6%. Subsequent experiments showed that the optimization algorithm demonstrated an understanding of the impact of austenite content on hydrogen embrittlement sensitivity in multiple experiments, and designed highstrength steel resistant to hydrogen embrittlement through the austenite content of martensitic steel.