The research group aims to conduct high-quality fundamental scientific and applied research in a limited number of clearly defined subject areas. In these fields, the group should maintain or acquire a strong, and where possible unique, position. The disciplines are supported by the ZAP members with a group of active PhD students and researchers around them; the ZAP members do not work in isolation, but in close cooperation, within and across the boundaries of the disciplines. The research focuseson modelling and numerical simulation, as well as extensive experimental support and validation. Wherever possible, the research group strives to consolidate and further develop successful lines of research. Within the MST research group, the following lines of research have been identified as strategically important: >Characterisation and modelling of mechanical material behaviour: The emphasis here lies on the experimental observation and numerical modelling of the mechanical behaviour of materials under impact-dynamic loads. Adapted test and measurement techniques will be developed for this purpose. The research cell currently has several test set-ups at its disposal with which the dynamic behaviour of a wide range of materials can be characterized. Based on the test results, material models are developed which, implemented in finite element codes, allow to predict the behaviour of structures under impact-dynamic loads. Characterisation and modelling of mechanical material behaviour therefore allows materials to be utilised optimally. Insights from this research (e.g. the influence of deformation parameters on the microstructure and related properties) can also be used in the design of innovative production methods such as "severeplastic deformation" to obtain materials with exceptional mechanical properties. >Physical materials science: Physical materials science is a generic field of knowledge in solid state materials science, which in the research cell is mainly applied on bulk metallic materials such as iron, aluminium, magnesium or titanium alloys. Physical materials science tries to understand the functional behaviour of engineering materials as a function of the solid state transformation mechanisms at the level ofthe microstructure (length scale 1 µm - 1 mm), the substructure (1 nm - 1 µm) and at the atomic scale (~0.1 nm). The research cell has built up an important reputation with regard to the crystallographic aspects of physical materials science. In particular, a solid knowledge has been built up in the field of the crystallographic texture of metal products (mainly steel and aluminium flat products). This knowledge relates both to the texture formation during the production process and to the relationship between texture and properties of the finished product. >Metal technology: This field of research aims to design and/or optimize industrial processes for the production of metals with improved functional properties based on the insights provided by physical materials science. Given the rich tradition in this field and given the existing research biotope including the presence of important research entities at the Ghent Tech Lane Campus (e.g. Ocas, CRM, Sirris, BIL, Clusta, Flamac) and the general materials science context in Flanders (with companies such as ArcelorMittal, Bekaert, Aleris Aluminium), this is undoubtedly a cornerstone of the research. The academic interpretation of this field requires a cross-material and cross-process approach. This manifests itself in the interest in various metals (ferrous and non-ferrous), in addition to the former almost exclusive steel research. This research is highly experimental in nature and makes intensive use of various material characterisation techniques. In recent years, a state-of-the-art electron microscopy laboratory has been built with 4 scanning electron microscopes (equipped with EBSD facilities, which is essential for texture measurements, among other things) and 2 transmissionelectron microscopes. > Computational Materials Science The research line "computational materials science" aims to gain insights into material properties and material synthesis/production by computational means, when these insights are experimentally inaccessible or difficult to access. This is done in two ways. The first is the 'ab initio' approach, in which the fundamental equations of quantum physics are solved numerically for a given material. This approach has grown from theoretical solid state physics, and is now sufficiently mature to be applied to complex materials. The second and more recent approach uses machine learning or artificial intelligence. Here, algorithms are trained in making connections between difficult to predict material properties on the one hand and datasets of easily determinable experimental or ab initio calculated data on the other hand. For a new material it is then sufficient to determine the easy data, in order to predict the difficult property using the trained algorithm.