Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

This study is motivated by discrepancies between recent experimental compression test data of nanoporus gold and the scaling laws for strength and elasticity by Gibson and Ashby. We present a systematic theoretical investigation of the relationship between microstructure and macroscopic behaviour of nanoporous metals. The microstructure is modelled by four-coordinated spherical nodes interconnected by cylindrical struts. The node positions are randomly displaced from the lattice points of a diamond lattice. We report scaling laws for Young’s modulus and yield strength, which depend on the extension of nodal connections between the ligaments and the solid fraction. A comparison with the scaling laws of Gibson and Ashby revealed a significant deviation for the yield stress. The model was applied for identifying a continuum constitutive law for the solid fraction. Matching the model’s predicted macroscopic stress–strain behaviour to experimental data for the flow stress at large compression strain requires the incorporation of work hardening in the constitutive law. Furthermore, the amount of disorder of the node positions is decisive in matching the model results to the experimental observations of an anomalously low stiffness and an almost complete lack of transverse plastic strain