In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials’ discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

Identification of elastic and plastic properties of materials from indentation tests received considerable attention in the open literature. However, unambiguous and automatic determination of parameters in the case of the crystal plasticity (CP) model is still an unsolved problem. In this paper, we investigate the possibility to unambiguously identify the CP parameters from spherical indentation tests using finite element method simulations combined with evolutionary algorithm (EA). To this aim, we check the efficiency and accuracy of EA while fitting either load–penetration curves, surface topographies, or both at the same time. By fitting the results against simulation data with known parameters, we can verify the accuracy of each parameter independently. We conclude that the best option is to fit both load–penetration curve and surface topography at the same time. To understand why a given fitting scheme leads to correct values for some parameters and incorrect values for others, a sensitivity analysis was performed.

Grain refinement due to severe plastic deformation is simulated using the crystal plasticity finite element method in the total Lagrangian setting. A rate-independent model with the regularized Schmid law is applied. As an example, a single pass of the equal channel angular pressing process is considered. Texture evolution, misorientation angle distributions and maps of new grains are presented. A special algorithm for tracking the creation of new grains in finite element simulations is developed. The results are analysed with respect to experimental data available in the literature. The possible mechanisms leading to the fragmentation of grains in a face centred cubic material are discussed. The influence of the quality of the microstructure representation on the simulation results is assessed.

A three-scale crystal plasticity model is applied to simulate microstructure evolution in hcp titanium subjected to cold rolling. Crystallographic texture and misorientation angle development, as an indicator of grain refinement, are studied. The impact of twinning activity on both phenomena is accounted for by combining the original three-scale formulation with the probabilistic twin-volume consistent (PTVC) reorientation scheme. The modeling results are compared with available experimental data. It is shown that the simulated textures are in accordance with the experimental measurements. The basic components of misorientation angle distribution, especially in the range of high angle boundaries, are also well reproduced.