EXTENDED ABSTRACT: High-strength and high-conductivity copper alloys are widely used as integrated structurefunction materials, such as lead frames in electronic chips and electrical contacts. These applications require the alloys to simultaneously exhibit high mechanical strength and high electrical conductivity. Achieving both properties is often challenging due to their inherent trade-off. Moreover, the comprehensive performance of copper alloys is influenced by a myriad of complex factors, including alloy composition and processing parameters. Optimizing both the alloy composition and processing concurrently to meet multi-objective performance requirements is essential for the refined improvement of industrial materials. However, due to the vast material parameter space, large-scale systematic optimization of composition and processing remains highly challenging in terms of cost and time.In this work, we introduce a high-throughput, iterative experimental optimization of the compositions and processing parameters of multi-component copper (Cu-Zr-Cr) alloys, assisted by machine learning (ML) methods. The ML sampling strategies are critical for achieving high efficiency and optimal performance in the iterative experiments. We compared different optimization processes guided by Bayesian optimization and Pareto front strategies, evaluating them based on the accuracy of the ML models and the target performances. The relationships among composition, processing, and properties are discussed for Cu-Zr-Cr alloys. Feature importance analyses reveal that aging processing is a critical factor. Finally, copper alloy samples with typical performance were subjected to optical, scanning, and transmission electron microscopy to analyze and discuss the relationship between alloy microstructure and performance.
Prof. Yi LIU obtained his Ph. D. degree at Materials Science and Engineering at Institute of Metal Research in China in 1997. Then he has worked in the field of computational materials science at Nagoya University, Japan (1997-2002); Juelich Research Center, Germany (2002-2003); University of Western Ontario, Canada (2003-2005); California Institute of Technology, US (2006-2012). He is a professor at Materials Genome Institute and Department of Physics at Shanghai University (2015-present) after working at the School of Materials Science and Engineering, the University of Shanghai for Science and Technology (2012-2015). His current research interests focus on the multi-paradigm materials design for advanced alloys, energy materials, and nanomaterials by combining computation (density functional theory and reactive force field molecular dynamics simulations), machine learning, and high-throughput experiment approaches.