2023/11/13 by Zhengdi Liu, Liu, Zhengdi, Xulong An +3
Engineering · #Additive Manufacturing Materials and Processes #Advanced Materials Characterization Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Microstructure and Mechanical Properties of Steels
paper · pdf · doi:10.48550/arxiv.2311.07174
openalex publication_date 2023/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When alloy systems comprise more than three elements, the visualization of the entire phase space becomes not only daunting but is also accompanied by a data surge. Addressing this complexity, we delve into the FeNiCrMn alloy system and introduce the Large CALPHAD Model (LCM). The LCM acts as a computational conduit, capturing the entire phase space. Subsequently, this enormous data is systematically structured using a high-dimensional phase diagram, aided by hash tables and Depth-first Search (DFS), rendering it both digestible and programmatically accessible. Remarkably, the LCM boasts a 97% classification accuracy and a mean square error of 4.80*10-5 in phase volume prediction. Our methodology successfully delineates 51 unique phase spaces in the FeNiCrMn system, exemplifying its efficacy with the design of all 439 eutectic alloys. This pioneering methodology signifies a monumental shift in alloy design techniques or even multi-variable problems.