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.
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Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials
Center for Complex and Active Materials
The primary mission of he MRSEC at UCI is to establish foundational knowledge in materials science and engineering of new classes of materials offering unique and broad functionality via an interplay among design, simulation, synthesis, and advanced characterization.