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Grain Boundary Engineering In Nickel-Rich Cathode: A Combination Of High-Throughput First-Principles And An Interpretable Machine Learning Study A synergistic approach combined high-throughput first-principles calculations and an interpretable machine learning (ML) framework to assess the feasibility of enhancing GBs through segregation-induced strengthening via doping. The results suggested using dopant elements that can effectively anchor and reinforce GBs, such as Mg, Al, Si, Ti, Cr, Mn, Fe, Cu, Zn, Hf, and Ce, some of which have already been used successfully in experiments.
The machine learning (ML) workflow includes feature preparation, feature engineering, ML model selection, and model application (brief representations of each process on the top and bottom). For GB strengthening through doping, elements should be placed at Ni sites and near the GB, rather than directly at the boundary itself. In additional, selecting a dopant with a lower period, higher bulk modulus, and smaller atomic radius increases the probability of GB strengthening.
Source: Acta Materialia 276 (2024) 120144 (DOI: 10.1016/j.actamat.2024.120144)
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