Physics-Based and Data-Driven Modelling of Process-Structure-Property (PSP) Linkage of Structural Metals

Dear Colleagues,

Metal forming/processing involves a series of thermo-mechanical deformations. Hierarchical structured materials develop during processing, which determines the final metal’s properties. An efficient approach to accelerate material development is to establish the Process–Structure–Property (PSP) linkages. This is beneficial to forward property prediction, which also enables finding optimal architected structures for given target properties in inverse material design. In addition, it accelerates the design, characterisation, evaluation, and deployment of metals.

Physics-based modelling has become an effective and efficient tool in material development due to increased computational resources, improved numerical algorithms, and progressed physical models. The application of machine learning and big data in materials science is unveiling hidden PSP relationships and can be harnessed in inverse design, e.g., optimizing processing and discovering materials. Combining materials informatics with computational materials science enables the closed-loop study of materials science, where computational materials science generates datasets and material informatics guides simulations.

This Special Issue aims to cover the latest advances in establishing PSP linkages using physics-based computational material science and machine learning methods. In this regard, original research papers, short communications, and review articles studying the following subjects are welcome in this Special Issue: metal forming/processing; microstructure characterisation; computational material science; machine learning; and data-driven materials design. 

Dr. Hui Wang
Dr. Lihong Su
Guest Editors

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• metal forming/processing
• plastic deformation
• mechanical properties
• mechanical testing
• microstructure characterisation
• computational material science
• machine learning
• data-driven material design