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Given the importance of drag model in solving fluid–particle interactions in unresolved numerical methods, this study proposed a machine learning (ML)-based drag model for irregular sand particles in transition flow, aided by spherical harmonic (SH) analysis and a resolved computational fluid dynamics-discrete element method (CFD-DEM). Initially, realistic particle shapes were reconstructed by the SH function, and their multi-scale shape features were quantified by the energy spectrums of SH frequencies. A developed fictitious domain method, particularly for irregularly shaped clumps, was proposed to solve fluid–solid interactions within resolved CFD-DEM. Subsequently, the fluid flow past a fixed particle test was repetitively simulated by the resolved CFD-DEM for 270 realistic sand particles, and a dataset consisting of 4220 drag coefficients was finally established. A classic ML algorithm, namely the multi-layer perceptron (MLP) neural network, was then utilized to train a drag model associated with the multi-scale shape features, particle orientations, and flow conditions. Compared with the results from the resolved CFD-DEM, the trained MLP model demonstrates both efficiency and accuracy in predicting the drag coefficients of natural sand particles with irregular shapes. This work provides a more reliable drag model for granular soils and shows its potential for application in large-scale modeling using the unresolved CFD-DEM framework. [ABSTRACT FROM AUTHOR]

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. In this paper, recent advances in data‐driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data‐driven processes. It is demonstrated how data‐driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data‐driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments. [ABSTRACT FROM AUTHOR]

A machine learning (ML) model was developed to predict the oxidation resistance, with the natural logarithm of the parabolic rate constant (ln k p) as the output. Five algorithms were used to training the model, revealing that backpropagation neural network and gradient boosting exhibited superior performance with an R 2 of 0.908 and 0.907. The trained models were employed to predict ln k p for changes in temperature, time, and elemental compositions. At last, promising areas with slower oxidation kinetics in typical commercial alloys were predicted under the guidance of the present ML methods. [Display omitted] • BPNN and GB exhibited superior performance, with an R2 of 0.908 and 0.907. • Temperature, Cr, time, and Ti are the most influential features. • Grand predict range: 4 atmospheres and 18 elements. • Model predicts oxidation kinetics for Inconel 718, 601, c-276, NiSiAlYPt. [ABSTRACT FROM AUTHOR]