Modeling of Dynamic Response of Metals
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 117
Special Issue Editor
Interests: molecular dynamics and coarsening modeling; dynamic deformation and damage of metal; energetic structural metals; metal-based composites
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The mechanical response of metallic materials is often accompanied by rich microstructural evolutions, which intercouple with the physical and mechanical properties of materials. Atomistic modeling plays a crucial role in understanding the microscopic mechanism of plastic deformation, phase changes, damage, and so on in metallic materials. In recent years, the rapid development of machine learning (ML) potentials and advanced multiscale simulation methods has provided possible solutions to the limitations of atomistic simulation on the scale of accuracy of interatomic potential and simulation, providing new possibilities for the atomistic modeling of metals. The aim of this Special Issue is to present the advanced achievements of atomistic modeling in the study of the mechanical response of metal materials.
Original research papers and critical reviews that focus on the following three topics are welcome: (i) new physical understanding of the mechanical response process of metallic materials, especially the structural evolution of plasticity, phase transformation, damage, etc.; (ii) multiscale simulation methods and machine learning (ML) potentials and their applications to metallic materials, including first-principles calculation, potential function development, coarse-grained modeling, etc.; (iii) the development of machine-learning-based approaches, including the training of artificial neural networks, the parameterization of micromechanical models, the verification and choice of models, etc., on the basis of big data generated by atomistic modeling.
Prof. Dr. Jianli Shao
Guest Editor
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Keywords
- plastic deformation
- phase transformation
- damage
- microstructure
- machine learning potentials
- multiscale modeling
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