High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys.

Nanolaminated structure with an average boundary spacing of 67 nm has been fabricated in copper by high-rate shear deformation at ambient temperature. The nanolaminated structure with an increased fraction of low angle grain boundaries exhibits a high microhardness of 2.1 GPa. The structure coarsening temperature is 180 K higher than that of its equiaxial nanograined counterpart. Formation of nanolaminated structure provides an alternative way to relax grain boundaries and to stabilize nanostructured metals with medium to low stacking faults energies besides activation of partial dislocations.

Utilized extensively in a myriad of industries, solid-solution copper alloys are prized for their superior electrical conductivity and mechanical properties. However, optimizing these often mutually exclusive properties poses a challenge, especially considering the complex interplay of alloy composition and processing techniques. To address this, we introduce a novel computational framework that employs advanced feature engineering within machine learning algorithms to accurately predict the alloy’s microhardness and electrical conductivity. Our methodology demonstrates a substantial enhancement over traditional data-driven models, achieving remarkable increases in R² scores—from 0.939 to 0.971 for microhardness predictions and from −1.05 to 0.934 for electrical conductivity. Through machine learning, we also spotlight key determinants that significantly influence overall performance of solid-solution copper alloys, providing actionable insights for future alloy design and material optimization.

Alloy gradient-grained structures (represented by copper as a typical single-phase face-centered cubic (FCC) metal), known for their superior mechanical properties such as enhanced strength, ductility, and fatigue resistance, have become increasingly important in aerospace and automotive industries. These alloys are often fabricated using advanced processing techniques such as laser welding, electron beam melting, and controlled cooling, which induce spatial gradients in grain size and optimize material properties by overcoming the traditional strength–ductility trade-off. In this study, a deep learning-based inversion framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed to efficiently predict key constitutive parameters, such as the initial critical resolved shear stress and hardening modulus, in alloy gradient-grained structures. The model integrates spatial features extracted from strain-field sequences and grain morphology images with temporal features from loading sequences, providing a comprehensive solution for path-dependent mechanical behavior modeling. Trained on high-fidelity Crystal Plasticity Finite Element Method (CPFEM) simulation data, the proposed framework demonstrates high prediction accuracy for the constitutive parameters. The model achieves an error margin of less than 5%. This work highlights the potential of deep learning techniques for the efficient and physically consistent identification of constitutive parameters in alloy gradient-grained structures, offering valuable insights for alloy design and optimization.