Learning Grain-Boundary Segregation: From First Principles to Polycrystals

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

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.

Crystallization mechanism of the Pt50Au50 alloy with grain boundary segregation during the solidification process

The crystalline mechanism of the Pt50Au50 alloy with grain boundary (GB) segregation during the rapid solidification process is investigated using molecular dynamics simulations. The cluster evolution and phase transformation processes during the GB segregation are analyzed by means of the energy temperature (E-T) curve, the pair distribution function (g(r)) curves, common neighborhood analysis (CNA), cluster-type index method (CTIM) and three-dimensional visualizing analyses. It is found that the GB segregation phenomenon of the Pt50Au50 alloy comes from various solidification temperatures of Pt- and Au-centered clusters. Four critical temperatures T1 (1153 K), T2 (1073 K), T3 (853 K) and T4 (753 K) are discovered during the liquid-solid transition, corresponding to the supercooled liquid, Pt-centered atom nucleation, Pt-centered cluster growth, Au-centered atom nucleation and grain growth process, respectively, which is observably different to the solidification process of other alloys. The Pt atoms begin to gather together in the high-temperature liquid before the liquid-solid transition. It is also found that the CTIM proposed by us would provide an effective tool to investigate the GB segregation process.

Accelerating the design of compositionally complex materials via physics-informed artificial intelligence

The chemical space for designing materials is practically infinite. This makes disruptive progress by traditional physics-based modeling alone challenging. Yet, training data for identifying composition-structure-property relations by artificial intelligence are sparse. We discuss opportunities to discover new chemically complex materials by hybrid methods where physics laws are combined with artificial intelligence.