Search

An intelligent alloy design strategy integrating machine learning and adaptive sampling is proposed and successfully applied to an example of martensitic stainless steel. Short iterative experiments prove the feasibility of this strategy in quickly finding new alloys with hardness higher than that of the current best and reasonably augmenting training data to create a high‐confidence prediction model. A credible relationship between the composition and hardness is demonstrated by the proposed model and the most promising candidate in the designed space is identified. In contrast to the traditional approaches, this strategy can meet the goal of global quest as it offers the advantages of flexibility, reliability, and efficiency. The suggested strategy can be extended to guide the experimental design of other materials. [ABSTRACT FROM AUTHOR]