The emergence of generative Artificial Intelligence (AI) and large language models has significantly enhanced the potential of machine learning and AI in the field 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 field is even more challenging. Applying cutting-edge AI technologies to alloy design can significantly improve design efficiency, shorten experimental cycles, and reveal underlying mechanisms, which is a major challenge we face. 

This webinar will discuss the application of machine learning and AI in the design of high-entropy alloys, showcasing how to efficiently develop near-zero thermal expansion high-entropy Invar alloys. Attendees of the webinar will also be introduced to PDGPT, a ChatGPT-based large language model designed to streamline the acquisition of magnesium alloys Phase Diagram information with high efficiency and accuracy. Enhanced by prompt-engineering, supervised fine-tuning and retrieval-augmented generation, PDGPT leverages the predictive and reasoning capabilities of large language models along with computational phase diagram data.

High-throughput CALPHAD calculations for screening and machine learning of refractory complex concentrated alloys │Tuesday February 4, 2025 at 2-3 PM CET/8-9 AM EST

Ziyuan Rao is an Associate Professor at Shanghai Jiao Tong University. He holds a PhD from RWTH Aachen University in Germany and has served as a postdoctoral researcher and group leader at the Max Planck Institute for Iron Research in Germany. In July 2024, he returned to China to join the School of Materials Science and Engineering at Shanghai Jiao Tong University. He has long been engaged in AI-based materials science research and has made a series of important contributions in cutting-edge fields such as and high-entropy alloys in recent years. He has published over 30 papers in journals such as Science.