Modern metallurgy of metals for extreme conditions is not much different than the methods used for Damascus steel 1000 years ago. Through trial and error, metallurgy has stumbled upon and settled into few excellent materials, that have been used consistently in extreme condition applications, such as nuclear reactors. The MASIF group, a world leader in Materials Informatics for mechanical deformation applications , focuses on the development of similarities, analogies and multiscale modeling, and aims at developing process-structure-property relationships in novel material classes, for identifying cheap, lightweight, strong and ductile materials at extreme conditions, namely high temperature or/and irradiation. Recent highlights include the development of deeper, multiscale understanding of deformation mechanisms in pure metals [2,3] and the development of a novel method for identifying yield points in materials through the use of camera-obtained surface map sequences .
The group's plan is to further establish and strengthen leadership in Materials Informatics, in Poland and worldwide. For this purpose, a book on "Materials Informatics" will be written (to be published in 2023-24) with modern approaches and insightful methods, with actual programming codes included, for handling data and promoting physics-informed machine learning in materials science. The MASIF group is also developing a Materials Informatics software that aims to promote machine learning solutions for experimenters in metallurgy and materials science. Moreover, the MASIF group aims to produce more than 5 PhD graduates that will be experts in materials informatics, machine learning, multiscale modeling and data science. Research-wise, the MASIF group will focus on a multi-threaded approach, that primarily includes deep understanding of the interplay between thermo-kinetic processes and mechanical deformation in the extreme conditions of high temperature and irradiation. A key for making progress is the development of efficient multiscale materials modeling approaches and interatomic potentials, by using machine learning methods. In addition, the focus will be on the use the long-developed rules-of-thumb in metallurgy to promote novel, automatically identified, analogies for materials informatics solutions and process-structure-property relationships that shall promote novel pathways in manufacturing.
- K. Frydrych, K. Karimi, M. Pecelerowicz, R. Alvarez, F.J. Dominguez-Gutiérrez, F. Rovaris, S. Papanikolaou, Materials informatics for mechanical deformation: A review of applications and challenges, Materials, 2021, 14, 5764.
- F.J. Domínguez-Gutiérrez, S. Papanikolaou, A. Esfandiarpour, P. Sobkowicz, M. Alava, Nanoindentation of single crystalline Mo: Atomistic defect nucleation and thermomechanical stability, Materials Science and Engineering: A, 826, 2021, 141912, ISSN 0921-5093, DOI:10.1016/j.msea.2021.141912
- Xu RG, Song H, Leng Y, Papanikolaou S. A Molecular Dynamics Simulations Study of the Influence of Prestrain on the Pop-In Behavior and Indentation Size Effect in Cu Single Crystals. Materials. 2021 Jan;14(18):5220.
- Papanikolaou S, Alava MJ. Direct detection of plasticity onset through total-strain profile evolution. Physical Review Materials. 2021 Aug 6;5(8):083602.
More on NOMATEN's scientific papers: http://nomaten.ncbj.gov.pl/papers-published-nomatens-team-members