The data-driven materials design paradigm has been largely applied in the research of materials science

to shorten the process from materials discovery to applications. As the key and hot pot in the research of data-driven materials design

machine learning can extract the pattern or law behind the data

of which the active-learning based strategy has been successfully employed to guide the exploitation of materials. Firstly

this paper introduces the main idea and elements of the active learning loop

as well as the methods/algorithms used in each step. Then the paper reviews the applications and effect of machine learning in the field of aero-engine materials including superalloys

titanium-based alloys

composites and thermal barrier coating. Considering the extreme service environment of aero-engine materials

the review is concluded by discussing the possible challenges and solutions.