Mechanics of Functional Materials

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.

Prof. Dr. Bai-Xiang Xu

, +49 6151 16-21906

Secretary: Ms. Maren Arnold

, +49 6151 16-21905

Building L6|01, Room 419, Otto-Berndt-Strasse 3, 64287 Darmstadt

Our Recent Publications

Ferroic Materials

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

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Energy Materials

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

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Advanced Processing

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

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Data-Driven Simulation

Our research focuses on exploring the applications of machine learning within the realm of modeling and designing microstructured materials. We delve into the latest advancements in machine learning techniques and their utilization in tackling complex issues such as multiscale simulation, microstructure-property correlation, and optimizing microstructures through inverse design. We also examine the existing challenges and potential pathways for future research. Our goal is to inspire and direct future endeavors in this critical domain, with the ultimate goal of establishing machine learning as a fundamental tool in material modeling and design practices.

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