• The Materials Structure, Informatics and Function (MASIF) group at NOMATEN deals with the search for relationships between the structure of materials and their properties.
• The data obtained in an experimental way or generated in various simulations are then processed using materials informatics.
• The current review of the literature in relation to the group research is published in the article Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges (Materials 2021, 14 (19), 5764).
• The work of the group at NOMATEN is led by Ph.D. Stefanos Papanikolaou.

Material structure studies, such as microscopic measurements, provide enormous amounts of data that can be used to reconstruct the microstructure of a material and become the basis for computer simulations on a molecular scale or larger. Understanding big data requires the use of statistical methods and machine learning, and simulations require efficient techniques to reconstruct the microstructure. Materials subjected to extreme conditions – such as irradiation or high temperatures – experience changes that are difficult to understand using traditionally used models. In such cases, artificial intelligence methods turn out to be irreplaceable in order to capture these changes and relate them to specific processes and physical properties taking place.

Experimental data (e.g. stress-strain curves, electron microscopy images of microstructures, strain maps from digital image correlation) are acquired by other research groups. In the MASIF group, on the other hand, simulations are performed on very different scales of space and time. Techniques such as:

• Density functional theory (DFT), based on a number of quantum-mechanical methods for modeling the structure of crystals and chemical particles,
• molecular dynamics (MD), a method of computer simulation that allows the study of the structure of materials, their properties and physical processes taking place in them (thermal conductivity, diffusion, radiation damage, etc.),
• simulations in the micro- and millimeter scale using fast Fourier transform (FFT) and the finite element method.

The obtained data sets are then processed using statistical methods (e.g. principal component analysis, PCA – principal component analysis, or discrete wavelet transform, DWT – discrete wavelet transform) and artificial intelligence (machine learning, deep learning).

„In this way, a lot of useful information can be obtained from existing datasets that would otherwise be lost,” notes Ph.D. Karol Frydrych from the NOMATEN Center of Excellence. „Using PCA or DWT, on the basis of deformation maps, it is possible to determine the moment when the material reaches the plastic state, which was described by Prof. Mikko Alava, director of NOMATEN, and Ph.D. Stefanos Papanikolaou in the article Direct detection of plasticity onset through total-strain profile evolution¹)”.

Thanks to deep learning, it is possible, for example, to find defects in photos taken from an electron microscope or to classify the microstructure of a material. „In this aspect, we can use materials informatics tools in cooperation with other NOMATEN research groups – for example, the material characterization group of Ph.D. Iwona Jóźwik or the functional properties of Prof. Łukasz Kurpaska” – adds Ph.D. Karol Frydrych.

An up-to-date review of the literature on issues related to group activity was recently published in the article Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges in Materials (2021, 14 (19), 5764) ²⁾

1) Stefanos Papanikolaou and Mikko J. Alava; Direct detection of plasticity onset through total-strain profile evolution; Phys. Rev. Materials 5, 083602; https://doi.org/10.1103/PhysRevMaterials.5.083602

2) Karol Frydrych, Kamran Karimi, Michal Pecelerowicz, Rene Alvarez, Francesco Javier Dominguez-Gutiérrez, Fabrizio Rovaris and Stefanos Papanikolaou; Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges; Materials 2021, 14(19), 5764[2]; https://doi.org/10.3390/ma14195764