Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Xing, Bin; Rupert, Timothy J.; Pan, Xiaoqing; Cao, Penghui

doi:10.1038/s41467-024-47927-9

Title: Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Journal Article · 09 May 2024 · Nature Communications

DOI: https://doi.org/10.1038/s41467-024-47927-9 · OSTI ID:2472188

Xing, Bin ^[1]; Rupert, Timothy J. ^[2];

^[2];

^[2]

Univ. of California, Irvine, CA (United States); OSTI
Univ. of California, Irvine, CA (United States)

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials 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 artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The 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.

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Research Organization:: Univ. of California, Irvine, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0022295

OSTI ID:: 2472188

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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