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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Neural Network Kinetics: Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials
Author(s)	Penghui Cao
On-Site Speaker (Planned)	Penghui Cao
Abstract Scope	The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and forming chemically ordered structures. In this talk, I will 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. Studying the temperature-dependent local chemical ordering in a refractory NbMoTa alloy, we reveal a critical temperature at which the B2 order reaches a maximum. 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.
Proceedings Inclusion?	Planned:

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