| Abstract Scope |
The Integrated Computational Materials Engineering (ICME) approach has transformed the design, development, and deployment of materials and products by enabling the prediction and optimization of composition, microstructure, and processing paths to meet specific property and performance targets, which may include strength, ductility, and corrosion resistance, depending on the application. Structure-property relationships used in ICME workflows are primarily based on experimental data, which significantly limits the capability for inverse design. Recent advances in modeling and simulations, high-throughput experimentation, and machine learning-based tools provide a promising alternative approach. Data from ab initio calculations, crystal plasticity, and representative volume element (RVE)-based simulations supplement the experimental data, now enhanced through high-throughput experiments. Advanced machine learning models are employed to develop predictive tools that capture complex relationships among composition, microstructure, and properties. This enables a more robust platform for inverse design of materials and products. A few illustrative examples will be presented. |