Edited by Stefanos Papanikolaou and Mikko Alava (National Centre for Nuclear Research, Poland)
The advance of multicomponent and high entropy represents a fantastic challenge as their multiple possible functions depend on a myriad of factors related to material microstructure, preparation and chemical composition. Materials science provides only hints to a possible theory for predicting the behavior, mechanical or/and multi-physics, of such complex alloys. Material informatics approaches try to bridge microstructural features of solids with their mechanical or/and multi-physics response, but they are confronted to the high level of complexity of microstructures and microscopic processes involved in materials. However, new machine learning approaches inspired from combinations of statistical physics and data science have emerged. The objective of this collection in Materials Theory is to gather interdisciplinary original contributions as well as reviews of recent advances in data science approaches to this rapidly growing field.
Articles will undergo all of the journal's standard peer review and editorial processes outlined in its submission guidelines