SYMPOSIUM 10: Integrated Computational-Experimental and Data-Driven Modeling and Design of Advanced Ceramics and Composites

Recent advances in computational materials science coupled with artificial intelligence (AI), machine learning (ML), and materials informatics approaches have significantly enhanced our understanding of fundamental phenomena in material behavior. These advances have contributed to improvement in materials performance, as well as discovery and the design of new materials and structures. Increasingly, the integration of computational modeling, data-driven approaches, and advanced experimental characterization is enabling accelerated materials discovery and the predictive design of next-generation ceramic and composite systems.  

This symposium solicits research on state-of-the-art physics-based, chemistry-aware, and data-driven modeling, advanced computational techniques, and modern machine learning architectures for a range of analysis, characterization, design of ceramics and composites with tailored properties. Approaches spanning computational research and experimental measurements across length and time scales are encouraged. Examples include, but are not limited to, ML-assisted novel microstructure/composite material design, generative and inverse materials design, establishing structure-property relationships in complex material microstructures, multiscale modeling through ML-driven coupling between scales, and AI-augmented experimental design and characterization. Additional topics of interest include high-throughput simulations and experiments, digital twins of materials systems, uncertainty quantification in predictive materials modeling and characterization, computational surrogate models for (multiphysics) behavioral predictions, and data-driven electromagnetic, thermal, chemical, frictional and mechanical response models. Of particular interest is also microstructure characterization, microstructure informatics, and image-based modeling, enabling the development of physically informed materials design strategies. A broader perspective is also encouraged, including research related to ceramic genome initiatives, virtual materials design platforms, novel strategies for materials processing-structure-performance relationships in advanced ceramics and composites for structural and functional applications. Contributions addressing modeling of surfaces, interfaces and grain boundaries across multiple scales in ceramics and composites are also encouraged. 

The symposium aims to bring together researchers working at the intersection of materials science, computational modeling, AI and advanced experimental characterization, providing a platform for discussion of emerging approaches for accelerated design and predictive understanding of ceramics and their composites. 

Proposed Session Topics 

• Materials informatics, artificial intelligence (AI), and machine learning for ceramics and composites 

• AI-enabled discovery, design, processing (including additive manufacturing), and characterization of ceramic and composite materials 

• Modeling of processing-structure-property relationships in advanced ceramics and composites 

• High-throughput materials discovery, design and characterization 

• Physics-informed machine learning for materials modeling, simulation and characterization 

• Microstructure informatics, microstructure engineering, and image-based materials modeling 

• Physics- and data-driven modeling of additively manufactured (3D printed) ceramics and composites 

• (Multi)functional ceramics and composites (e.g. batteries, fuel cells and piezoceramics)- multiphysics modeling, characterization and design 

• Multiphysics materials behavior and degradation of ceramics, coatings and composites under extreme environments (thermal, mechanical, chemical and radiation) 

• Fracture and damage mechanics of ceramics and composites across scales 

• Modeling of surfaces, interfaces, defects, heterogeneities, and grain boundaries at multiple scales 

• Friction, wear, and tribology in ceramics and composites 

• Sensing/actuating materials modeling and smart ceramic systems 

• Digital twins and predictive modeling for ceramics and composites 

 

Symposium Organizers 

• Gerard L. Vignoles, University of Bordeaux, France 

• Sathiskumar Anusuya Ponnusami, Queen Mary University of London, UK  

• Jingyang Wang, Liaoning Academy of Materials, China 

• Ghatu Subhash, University of Florida, USA  

• Joaquin Garcia Suarez, École Polytechnique Fédérale de Lausanne, Switzerland 

• Vignesh Kannan, École Polytechnique, France 

• Peter Kroll, University of Texas at Arlington, USA 

• Jian Luo, University of California, San Diego, USA 

• Yixiu Luo, Institute of Metal Research, Chinese Academy of Sciences, China 

• Sergei Manzhos, Tokyo Institute of Technology, Japan 

• Bin Liu, Shanghai University, China 

• Katsuyuki Matsunaga, Nagoya University, Japan 

• Paul Rulis, University of Missouri-Kansas City, USA 

• Yan LI, Dartmouth College, USA 

Points of Contact 

• Gerard L. Vignoles; vinhola@lcts.u-bordeaux.fr 

• Sathiskumar Anusuya Ponnusami; s.a.ponnusami@qmul.ac.uk  

• Jingyang Wang; jywang@lam.ln.cn  

• Ghatu Subhash; subhash@ufl.edu