Latest Accepted Articles

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Viscoelastic behaviors of amorphous alloys in the framework of the quasi-point defect theory
QIAO Jichao, ZHANG Langting, XING Guanghui, HAO Qi, LIANG Shuyi, CUI Jingbo, DUAN Yajuan
, Available online  , doi: 10.6052/1000-0992-25-015
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Abstract:
Amorphous alloys exhibit complex viscoelastic behaviors due to their unique atomic structure, characterized by dynamic relaxation and static hysteresis features. This not only serves as a core entry point for in-depth understanding of fundamental physical issues such as glass transition, plastic deformation mechanisms, and dynamic heterogeneity, but also provides key theoretical support for the development and engineering application of high-performance amorphous alloys. Currently, how to construct a theoretical framework from the microscopic mechanism that can uniformly describe and predict their complex viscoelastic behaviors remains a core challenge in this field. This paper focuses on the core role and latest progress of the Quasi-Point Defect (QPD) theory in systematically analyzing the viscoelastic behaviors. It deeply explores the application of the QPD theory in analyzing dynamic relaxation and reveals the intrinsic consistency between this theory and fractional models. On this basis, it reviews the intrinsic connection between dynamic relaxation and macroscopic quasi-static viscoelastic deformation, and explains the physical mechanisms behind phenomena such as two-step relaxation and creep, which are dominated by defect movements at different scales. Regarding creep behavior, it particularly discusses the understanding of defect evolution and multi-level power-law creep mechanisms. Additionally, this paper systematically expounds the mechanism of regulating the energy state of amorphous alloys through viscoelastic deformation and how this regulation changes the dynamic relaxation of the material by influencing the concentration, distribution, and cooperative movement of quasi-point defects. This paper aims to demonstrate how to establish the correlation between the microstructure heterogeneity, defect dynamics, and viscoelastic response of amorphous alloys based on the QPD theory, providing a theoretical perspective for understanding and predicting their complex mechanical behaviors.
Intelligent Prediction of Mechanical Properties in Metallic Materials based on Machine Learning: A Review & Perspective
CAO Zhizeng, WANG Guiji, LUO Binqiang
, Available online  , doi: 10.6052/1000-0992-25-026
Abstract(128) HTML (15) PDF(106)
Abstract:
The rapid advancement of machine learning is transforming the research paradigm of mechanical properties of metallic materials from experience-driven to data-driven. This review systematically summarizes recent progress and challenges in machine learning based prediction of mechanical properties in metallic materials. We first outline commonly used ML algorithms and workflows, with an emphasis on cutting-edge methods such as explainable AI and physics-informed machine learning. We then review typical applications and predictive performance of ML models across three scales: micro/mesoscopic properties (e.g., microstructural evolution, fracture behavior), macroscopic properties (e.g., hardness, stress response, fatigue life), and cross-scale coupling properties (e.g., flow stress, yield strength, constitutive parameter inversion), highlighting their advantages in high-throughput computation and multi-scale modeling. Finally, we identify persistent challenges such as data scarcity, heterogeneity, and insufficient accuracy under wide temperature/strain-rate ranges, and propose potential solutions including transfer learning, large language models, and multi-modal fusion. Looking forward, we outline a technical pathway integrating multi-modal data and physical mechanisms for accurate prediction of mechanical behavior under extreme conditions, aiming to advance materials mechanics toward digitalization and intelligence.