2024 IRG-1: Neural Network Kinetics: Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials
•We introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments.
•The framework is grounded on efficient on-lattice structure and chemistry representation combined with neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps.
•Using this method, we study the temperature-dependent local chemical ordering in refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum.
•Our atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure nucleation.
Bin Xing, Wanjuan Zou, Timothy J. Rupert, Xiaoqing Pan, Penghui Cao University of California, Irvine