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Recent Computational Approaches for Accelerating Dendrite Growth Prediction: A Short Review

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Abstract

Phase-field modeling plays a pivotal role in the study of microstructure evolution, offering a crucial tool for understanding and predicting changes in material systems based on thermodynamics. This modeling approach excels in predicting the intricate side-branching patterns characteristic of dendritic growth, without explicit tracking of solid–liquid interfaces. However, the phase-field method often incurs significant computational costs, as it typically involves solving a system of coupled partial differential equations for a set of continuous field variables. Moreover, spatial discretization must be fine enough to resolve microstructure features like grain boundaries or side-branches. Additionally, obtaining an understanding of the effects of material properties often requires a substantial number of simulation results. To address these challenges, recent advances have combined high-throughput phase-field simulations with machine learning techniques, enabling the discovery of analytical expressions for specific material properties based on varying parameters. This approach offers valuable guidance for experimental design, significantly shortening development timelines and reducing costs for new materials. Although high-throughput phase-field simulations combined with machine learning have reduced the number of required databases, they still necessitate enough simulations for database construction. To further expedite this process, acceleration approaches like graphics processing unit (GPU)-based phase-field simulation and the utilization of physics-informed neural networks (PINNs) for phase-field modeling are being explored. With ongoing advancements in computational speed, the integration of machine learning into material development processes is increasingly regarded as an efficient method for further accelerating the pace of material innovation.

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Acknowledgements

This work was supported by the Korea Institute for Advancement of Technology funded By the Ministry of Trade, Industry& Energy (MOTIE, Korea) (No.P0018649, Fuel Cell Element Part and System Technology Development for Fuel Cell Powered Tram). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2020R1A2C2010986, 2022M3H4A1A04085301).

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  1. Department of Mechanical Engineering, Sogang University, Seoul, 04107, South Korea

    Hyunjoo Lee & Dongchoul Kim

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Lee, H., Kim, D. Recent Computational Approaches for Accelerating Dendrite Growth Prediction: A Short Review. Multiscale Sci. Eng. 5, 119–125 (2023). https://doi.org/10.1007/s42493-024-00098-7

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