We are devoted to description and optimization of microstructure and the related mechanical and functional properties in functional and energy materials by multiphysics models and numerical simulations. Material properties rely on the microstructure of the materials and its evolution under multiphysics stimuli. We therefore apply phase-field method, micromechanics theory and multiscale approaches to develop the models, and implement them by using numerical methods such as finite element and finite difference. We apply parallel computing and run large-scale simulations on high performance clusters. Additionally, in all our research topics, Machine Learning and Data-Driven techniques become increasingly more important tools.
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Ferroics such as ferroelectrics, ferromagnetics, and ferroelastics are functional materials widely used in modern electronics. The development of high-level miniaturized and integrated devices requires the prediction of material properties for a given target performance.
DOI 10.1038/s41467-017-00059-9 and DOI 10.1103/PhysRevApplied.8.014011
The storage of energy is one of the most pressing issues of modern society, in particular for mobile devices and electric vehicles. The predominant technology are lithium-ion batteries that suffer, however, from a gradual deterioration of their capacity and power, which is strongly affected by mechanical effects such as delamination, plastic deformation, or crack growth.
DOI 10.1002/gamm.201610006 and DOI 10.1016/j.cma.2016.04.033
Advanced processing methods have gained wide interest for industrial applications. The prediction of microstructures resulting from these methods is of great interest and can complement time- and cost-expensive experiments.
DOI 10.1016/j.scriptamat.2020.05.016 and DOI 10.1038/s41524-019-0219-7
The mesoscopic properties of functional materials rely on their characteristic feature structures on the microscopic scales. To evaluate the microstructure-property correlations, the micro-meso multiscale approach like homogeneization and static approach using Machine Learning algorithms are applied. Moreover, to ensure models with physical parameters, atomistic-continuum multiscale simulations are also attempted through cooperation with other theoretical groups.