Additive manufacturing of alloys has enabled the creation of machine parts that meet the complex requirements needed to optimize performance in aerospace, automotive, and energy applications. Finding the ideal mix of elements to use in these parts when there are countless possible combinations available is a complicated process that has been accelerated by computational tools and artificial intelligence.
With large language models (LLM), such as ChatGPT, evolving to better understand natural languages, researchers in the Materials Science and Engineering Department at Carnegie Mellon University have pioneered the potential to train LLM to understand a novel alloy physics language in a similar manner. Led by Mohadeseh Taheri- Mousavi, they have developed AlloyGPT, which recognizes the relationship between composition, structure, and properties in order to generate novel designs for additively manufacturable structural alloys.
Source: College of Engineering
Assistant Professor Mohadeseh Taheri-Mousavi and postdoctoral researcher Bo Ni showcase a paradigm of the AlloyGPT model.
The AlloyGPT model, detailed in a recent paper published in npj Computational Materials, is unique in that it has dual functionality. It can accurately predict multiple phase structures and properties based on given alloy compositions, and conversely, it can suggest a comprehensive list of alloy compositions that meet given desired design goals.
