Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Gao, Jianbao; Zhong, Jing; Liu, Guangchen; Zhang, Shaoji; Zhang, Jiali; Liu, Zuming; Song, Bo; Zhang, Lijun
• Is Part Of: Science and technology of advanced materials. Volume 24:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Alloy design -- casting aluminum alloy -- high-throughput calculations -- CALPHAD -- active learning; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2023.2196242
• Languages: English
• ISSNs: 1468-6996

Abstract

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. GRAPHICAL ABSTRACT: uf0001