EXTENDED ABSTRACT: The emergence of generative Artificial Intelligence (AI) and large language models has signiffcantly enhanced the potential of machine learning and AI in the ffeld of materials design. For example, the success of AlphaFold in protein structure prediction has demonstrated the powerful capabilities of generative AI in uncovering complex relationships between material composition, structure, and performance. However, the challenges in alloy design are even more daunting, with a broader compositional space, more complex synthesis processes, and deeper intrinsic mechanisms. Coupled with the smaller experimental datasets, the application of AI in this ffeld is even more challenging. Applying cutting-edge AI technologies to alloy design can signiffcantly improve design efficiency, shorten experimental cycles, and reveal underlying mechanisms, which is a major challenge we face. This report briefly discusses the application of machine learning and AI in the design of high-entropy alloys and high-temperature alloys, showcasing how we efficiently developed near-zero thermal expansion high-entropy Invar alloys and optimized heat treatment parameters for high-temperature alloys using machine learning.

Keywords: Artiffcial Intelligence ;Alloy design;high-entropy alloys;high-temperature alloys

REFERENCES:

[1] Rao Z, Tung P Y, Xie R, et al. Machine learning–enabled high-entropy alloy discovery. Science, 2022, 378(6615): 78-85.