Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Jianbao Gao; Jing Zhong; Guangchen Liu; Shaoji Zhang; Jiali Zhang; Zuming Liu; Bo Song; Lijun Zhang

doi:10.1080/14686996.2023.2196242

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Sci Technol Adv Mater. 2023 Apr 11;24(1):2196242. doi: 10.1080/14686996.2023.2196242. eCollection 2023.

Authors

Jianbao Gao¹, Jing Zhong¹, Guangchen Liu², Shaoji Zhang¹, Jiali Zhang¹, Zuming Liu¹, Bo Song³, Lijun Zhang¹

Affiliations

¹ State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan, P.R. China.
² Mechanical and Materials Engineering Department, Worcester Polytechnic Institute, Worcester, MA, USA.
³ State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, WH, P.R. China.

Abstract

Scandium is the best alloying element to improve the mechanical properties of industrial Al-Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc additions in different commercial Al-Si-Mg casting alloys with well-defined compositions. However, no attempt to optimize the contents of Si, Mg, and Sc has been made due to the great challenge of simultaneous screening in high-dimensional composition space with limited experimental data. In this paper, a novel alloy design strategy was proposed and successfully applied to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys over high-dimensional composition space. Firstly, high-throughput CALculation of PHAse Diagrams (CALPHAD) solidification simulations of ocean of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide composition range were performed to establish the quantitative relation 'composition-process-microstructure'. Secondly, the relation 'microstructure-mechanical properties' of Al-Si-Mg-Sc hypoeutectic casting alloys was acquired using the active learning technique supported by key experiments designed by CALPHAD and Bayesian optimization samplings. After a benchmark in A356-xSc alloys, such a strategy was utilized to design the high-performance hypoeutectic Al-xSi-yMg alloys with optimal Sc additions that were later experimentally validated. Finally, the present strategy was successfully extended to screen the optimal contents of Si, Mg, and Sc over high-dimensional hypoeutectic Al-xSi-yMg-zSc composition space. It is anticipated that the proposed strategy integrating active learning with high-throughput CALPHAD simulations and key experiments should be generally applicable to the efficient design of high-performance multi-component materials over high-dimensional composition space.

Keywords: Alloy design; CALPHAD; active learning; casting aluminum alloy; high-throughput calculations.

Grants and funding

This work was supported by the Science and Technology Program of Guangxi province, China [Grant No. AB21220028], the National Natural Science Foundation of China [Grant No. U2102212], the Natural Science Foundation of Hunan Province for Distinguished Young Scholars [Grant No. 2021JJ10062], the Youth Talent Project of Innovation-driven Plan at Central South University [Grant No. 2282020cxqd027] and the Lvyangjinfeng Talent program of Yangzhou. Jianbao Gao acknowledges the financial support from the Fundamental Research Funds for the Central Universities of Central South University [Grant No. 2019zzts854]. Jing Zhong acknowledges the financial support from the Youth Fund of the National Natural Science Foundation of China [Grant No. 52101028], China Postdoctoral Science Foundation [Grant No. 2021M703628]. The authors would like to thank Mr. Yi Wang, Mr. Tongdi Zhang and Ms. Shenglan Yang from State Key Laboratory of Powder Metallurgy, Central South University for kindly helping and discussing.