@misc{huber_editorial_machine_2020, 
author={Huber, N., Kalidindi, S., Klusemann, B., Cyron, C.},
title={Editorial: Machine Learning and Data Mining in Materials Science},
year={2020},
howpublished = {Other: editorial},
doi = {https://doi.org/10.3389/fmats.2020.00051},
abstract = {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.},
note = {Online available at: \url{https://doi.org/10.3389/fmats.2020.00051} (DOI). Huber, N.; Kalidindi, S.; Klusemann, B.; Cyron, C.: Editorial: Machine Learning and Data Mining in Materials Science. Frontiers in Materials. 2020. vol. 7, 51. DOI: 10.3389/fmats.2020.00051}}