Deformation mechanisms of ultra-elastic high entropy intermetallic alloy unveiled by large-scale machine learning-enabled molecular simulations
Summary of Achievement
Dr. Chun-Wei Pao's research team has conducted an in-depth collaborative study combining atomistic scale machine learning model and experiments to investigate the mechanical properties of the complex alloy CoNiHfTiZr, known for its ultraelasticity and Elinvar effects.In this study, the experimental team from the City University of Hong Kong discovered that this complex alloy exhibits high strength while retaining high ductility beyond its elastic limit. To provide comprehensive insights into such extraordinary plastic behaviors, Dr. Pao's team trained a machine learning-based predictive model for CoNiHfTiZr complex alloy with quantum accuracy, allowing large-scale atomistic simulations with system sizes orders of magnitude beyond that of the ab initio calculations. Through a series of large-scale molecular simulations, they successfully captured the dislocation motion and its mechanisms in this highly distorted complex alloy. The study revealed the correlations between dislocation migration, temperature, and microstructure, thereby providing a microscopic explanation for the unique mechanical properties of this complex alloy. The work have been published in the journal Nature Communications.
