%0 editorial
%@ 2296-8016
%A Huber, N., Kalidindi, S., Klusemann, B., Cyron, C.

%D 2020
%J Frontiers in Materials

%P 51 
%R doi:10.3389/fmats.2020.00051
%T Editorial: Machine Learning and Data Mining in Materials Science
%U https://doi.org/10.3389/fmats.2020.00051
 
%X The development of new materials, incorporation of new functionalities, and even the description of well-studied materials strongly depends on the capability of individuals to deduce complex structure-property relationships. A significant challenge in this field remains the “curse of dimensionality”. Even for the characterization of moderately complex materials, often a considerable number of parameters is required to characterize their composition and microstructure (or also processing conditions) uniquely. Modeling of materials is thus facing the challenge of high-dimensional parameter spaces, where numerous parameter combinations have to be sampled and studied thoroughly. Relying thereby on experiments is typically prohibitively expensive, given the often high-dimensional parameter space of interest. Thus, the combination of experimental and computational approaches is receiving increasing attention. The complex interdependencies in the resulting data sets can be studied using machine-learning approaches. Artificial neural networks and data-driven approaches can significantly help to identify, approximate, and visualize structure-property relationships of interest. This way, they can accelerate our understanding and effective utilization of complex hierarchical materials.