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Developing refractory high-entropy superalloys (RSAs) with performance advantages over nickel-based alloys is a critical frontier in materials science. Body-centered cubic (bcc)-based RSAs have attracted significant attention, with ruthenium (Ru) playing a key role in forming two-phase regions of A2 (disordered bcc) + B2 (ordered bcc), which could lead to superalloy-like microstructures. This study introduces the application of the Kolmogorov-Arnold Network (KAN) model to predict the mechanical and thermodynamic properties of Ru while comparing its performance against other commonly used machine-learned models. Utilizing density functional theory calculations as training data, the KAN model demonstrates superior accuracy and computational efficiency compared to conventional methods, while reducing descriptor complexity. The model accurately predicts a range of properties, including elastic constants, thermal expansion coefficients, and various moduli, with discrepancies within 6% of experimental reference data. Molecular dynamics simulations further validate the model’s efficacy, accurately capturing Ru’s phase transitions from hexagonal close-packed (hcp) to face-centered cubic structure and the melting point. This work presents the first application of KAN in materials science, demonstrating how its balanced performance and efficiency provide a new pathway for designing advanced materials, with unique advantages over conventional machine learning approaches in predicting material properties. 

Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties. 

Solid polymer electrolytes (SPEs) offer a safer battery electrolyte alternative but face design challenges. This review highlights applications of machine learning alongside theory-based models to improve SPE design.