We are excited to share with you our latest selection of articles in the field of Computational Materials Science, curated for your interest and professional development.
In this collection, you will find cutting-edge research and groundbreaking studies that explore innovative methodologies, novel materials, and advanced computational techniques. Our featured articles include:
1. Phase-Field Simulation of δ Hydride Precipitation with Interfacial Anisotropy
Abstract: This study investigates the impact of anisotropic interfacial energy and mobility on hydride precipitation in zirconium alloys using the phase-field method. Results show anisotropic hydrides form parallelogram-like and needle-like morphologies, aligning with experimental observations, unlike isotropic hydrides' slate-like forms. The findings highlight that semi-coherent or non-coherent interfaces better adjust lattice mismatches, lowering gradient energy and influencing precipitation rates, which aids future research on hydride precipitation orientation and properties.
Full-text Link: https://doi.org/10.32604/cmc.2023.044510
2. Machine Learning Design of Aluminum-Lithium Alloys with High Strength
Abstract: This study presents a machine learning strategy for designing high-strength aluminum-lithium alloys, significantly reducing development time and cost. Radial basis function (RBF) neural networks outperform back propagation (BP) networks, predicting tensile and yield strengths accurately. Validation shows Al-2Li-1Cu-3Mg-0.2Zr alloy achieves higher strengths, demonstrating the model's reliability and efficiency in alloy design.
Full-text Link: https://doi.org/10.32604/cmc.2023.045871
3. Material-SAM: Adapting SAM for Material XCT
Abstract: This study presents Material-SAM, a fine-tuned Segment Anything model for XCT image segmentation of carbide in nickel-based superalloys. It outperforms SAM and U-Net models, achieving 88.45% CPA and 88.77% DSC, with significantly lower input requirements.
Full-text Link: https://doi.org/10.32604/cmc.2024.047027
4. A Hybrid Level Set Optimization Design Method of Functionally Graded Cellular Structures Considering Connectivity
Abstract: This study introduces a Hybrid Level Set Method (HLSM) to enhance interface connectivity in functionally graded cellular structures, addressing challenges in topology optimization and additive manufacturing. The method blends heterogeneous microstructure interfaces, optimizing 2D and 3D materials, and improving design robustness.
Full-text Link: https://doi.org/10.32604/cmc.2024.048870
5. MD Simulation of Diffusion Behaviors in Collision Welding Processes of Al-Cu, Al-Al, Cu-Cu
Abstract: This study uses molecular dynamics simulations to investigate atomic diffusion behaviors at collision interfaces in Cu-Al, Al-Al, and Cu-Cu combinations. It finds that diffusion in dissimilar metals (Cu-Al) depends on transverse velocity, while similar metals (Al-Al, Cu-Cu) depend on longitudinal velocity, offering insights for optimizing welding parameters.
Full-text Link: https://doi.org/10.32604/cmc.2024.048644
6. Multiscale Simulation of Microstructure Evolution during Preparation and Service Processes of Physical Vapor Deposited c-TiAlN Coatings
Abstract: This study proposes a multi-scale simulation approach to optimize the quality of PVD TiAlN coatings for cutting tools. By integrating phase-field simulation, cutting simulation, and Cahn-Hilliard modeling, it correlates service temperature with microstructure evolution, aiding in property design and lifespan prediction.
Full-text Link: https://doi.org/10.32604/cmc.2024.051629
7. Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm
Abstract: This study utilizes an ML-based approach combined with genetic algorithms to design copper alloys with high strength and high electrical conductivity. The Catboost model, achieving over 93.5% accuracy, correlates alloy composition with target properties. Data augmentation and Pareto front refinement optimize compositions, with predictions validated against literature data, confirming reliability.
Full-text Link: https://doi.org/10.32604/cmc.2024.042752
These articles represent significant contributions to the field and offer valuable insights into current trends and future directions. We believe they will be of great interest to you and your colleagues. To access the full articles, please visit our journal’s website: https://www.techscience.com/journal/cmc
We hope you find these readings inspiring and informative. Should you have any questions or require further information, please do not hesitate to contact us. Thank you for your continued support and interest in our journal.
- Badges
- Science topic