  Diffusion involving atom transport from one location to another governs many
important processes and behaviors such as precipitation and phase nucleation.
Local chemical complexity in compositionally complex alloys poses challenges
for modeling atomic diffusion and the resulting formation of chemically ordered
structures. Here, 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 a refractory Nb-Mo-Ta 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 formation. The scalable
NNK framework provides a promising new avenue to exploring diffusion-related
properties in the vast compositional space within which extraordinary
properties are hidden.
