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High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significant challenges in uncovering fundamental physical principles and effectively guiding alloy design. Traditional experimental approaches, often reliant on trial-and-error methods, are time-consuming, cost-prohibitive, and inefficient. To accelerate progress in this field, advanced simulation techniques and data-driven methodologies, particularly machine learning (ML) with a particular interest in nanoscale phenomena, have emerged as transformative tools for composition design, property prediction, and performance optimization. By leveraging extensive materials databases and sophisticated learning algorithms, ML facilitates the discovery of intricate patterns that conventional methods may overlook, and enables the design of HEAs with targeted properties. This review paper provides a comprehensive overview of recent advancements in ML applications for HEAs. It begins with a brief introduction of the fundamental principles of HEAs and ML methodologies, including key algorithms, databases, and evaluation metrics. The critical role of materials representation and feature engineering in ML-driven HEA design is thoroughly discussed. Furthermore, state-of-the-art developments in the integration of ML with HEA research, particularly in composition optimization, property prediction, and phase identification, are systematically reviewed. Special emphasis is placed on cutting-edge deep learning techniques, such as generative models and computer vision, which are revolutionizing the f ield. This study explores the application of machine learning (ML) in developing highly accurate ML interatomic potentials (MLIPs) for molecular dynamics (MD) simulations. These MLIPs have the potential to enhance the accuracy and efficiency of simulations, enabling a more precise representation of the fundamental physics governing high-entropy alloys (HEAs) at the atomic level. A critical discussion is provided, addressing both the potential advantages and the inherent limitations of this approach. This review aims to provide insights into the future directions of ML-driven HEA research, offering a roadmap for advancing material design through data-driven innovation. 

Biological materials have consistently intrigued researchers due to their remarkable properties and intricate structure–property-function relationships. Deciphering the pathways through which nature has bestowed its exceptional properties represents a complex challenge. The hierarchical architectures of biomaterials are recognized as the basis for mechanical robustness. Moreover, it is well-established that the intriguing properties of biomaterials arise primarily from the architecture at the nanoscale, particularly the abundant carefully designed interfaces. Driven by the diverse functionality and the increasing comprehension of the underlying design mechanisms in biomaterials, substantial endeavors have been directed toward emulating the architectures and interactions in synthetic materials. By reviewing atomistic modeling of nacre, wood, and coconut endocarp, in this work, we aim at highlighting the significant role of atomistic modeling in revealing nanoscale strengthening and toughening mechanisms of biomaterials, subsequently advancing the development of bioinspired material. 

In this paper, we present concurrent atomistic-continuum (CAC) simulations of the hydrogen (H) diffusion along a grain boundary (GB), nearby which a large population of dislocations are piled up, in a plastically deformed bi-crystalline bcc iron sample. With the microscale dislocation slip and the atomic structure evolution at the GB being simultaneously retained, our main findings are: (i) the accumulation of tens of dislocations near the H-charged GB can induce a local internal stress as high as 3 GPa; (ii) the more dislocations piled up at the GB, the slower the H diffusion ahead of the slip–GB intersection; and (iii) H atoms diffuse fast behind the pileup tip, get trapped within the GB, and diffuse slowly ahead of the pileup tip. The CAC simulation-predicted local H diffusivity, Dpileup−tip, and local stresses, σ, are correlated with each other. We then consolidate such correlations into a mechanics model by considering the dislocation pileup as an Eshelby inclusion. These findings will provide researchers with opportunities to: (a) characterize the interplay between plasticity, H diffusion, and crack initiation underlying H-induced cracking (HIC); (b) develop mechanism-based constitutive rules to be used in diffusion–plasticity coupling models for understanding the interplay between mechanical and mass transport in materials at the continuum level; and (c) connect the atomistic deformation physics of polycrystalline materials with their performance in aqueous environments, which is currently difficult to achieve in experiments. 

The coconut shell consists of three distinct layers: the skin-like outermost exocarp, the thick fibrous mesocarp, and the hard and tough inner endocarp. In this work, we focused on the endocarp because it features a unique combination of superior properties, including low weight, high strength, high hardness, and high toughness. These properties are usually mutually exclusive in synthesized composites. The microstructures of the secondary cell wall of the endocarp at the nanoscale, in which cellulose microfibrils are surrounded by hemicellulose and lignin, were generated. All-atom molecular dynamics simulations with PCFF force field were conducted to investigate the deformation and failure mechanisms under uniaxial shear and tension. Steered molecular dynamics simulations were carried out to study the interaction between different types of polymer chains. The results demonstrated that cellulose–hemicellulose and cellulose–lignin exhibit the strongest and weakest interactions, respectively. This conclusion was further validated against the DFT calculations. Additionally, through shear simulations of sandwiched polymer models, it was found that cellulose–hemicellulose-cellulose exhibits the highest strength and toughness, while cellulose–lignin-cellulose shows the lowest strength and toughness among all tested cases. This conclusion was further confirmed by uniaxial tension simulations of sandwiched polymer models. It was revealed that hydrogen bonds formed between the polymer chains are responsible for the observed strengthening and toughening behaviors. Additionally, it was interesting to note that failure mode under tension varies with the density of amorphous polymers located between cellulose bundles. The failure mode of multilayer polymer models under tension was also investigated. The findings of this work could potentially provide guidelines for the design of coconut-inspired lightweight cellular materials.