Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength

Advanced aluminum-lithium alloys are the key structural materials urgently needed for the development of light-weighted aircraft in the aerospace field. In this study, we employ a machine learning approach accompanied by domain knowledge to realize the accelerated design of aluminum-lithium alloy with high specific modulus and specific strength by identifying an optimal combination of key features through a three-step feature filtering of datasets containing 145 alloys. The maximum specific modulus in the experimental alloys that screened from the predicted results increases by 4 % compared with the maximum specific modulus in the comparative dataset. The specific modulus of the designed alloy with the best comprehensive performance increased by 12.6 % compared with the widely used 2195-T8 alloy while maintaining a similar specific strength. Machine learning shows appealing feasibility and reliability in the field of materials design.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).