Databases ( left) generated through microscopy and (nano)mechanics experiments are combined with multiscale modeling simulations to provide relevant descriptors and predictors. Typical materials informatics and machine learning workflow for mechanical deformation. But, also, it may involve topological measures, such as coordination number, packing efficiency, and local ordering, lattice distortion, and local crystallography in metallic systems. This information may cover a broad range of attributes including element-wise atomic properties and atomic fractions in multicomponent materials.

In the informatics study of structure-property relationships, a relevant material dataset contains a feature space normally spanned by a set of atomic and/or local (micro-)structural descriptors that presumably capture salient physical mechanisms and microstructural details for an existing set of materials. The sources of data are typically diverse, including those (re)produced from high throughput (ab initio, atomistic, and continuum-based) simulations, and extensive experimentation (typically, mechanical testing and microscopy) and/or those collected from existing materials databases. The former demands continuous access to sufficient amounts of data which are essential for any quantitative assessment of materials behavior. ML provides possible frameworks to integrate search strategies for novel materials with tailored properties in a timely, yet robust and efficient manner, which is otherwise impossible using traditional frameworks.ДЄs schematically illustrated in Figure 1, the implementation of an ML-based approach involves two elementary steps: pattern detection of input experimental or/and simulation data, and predictions, both essential in understanding fundamental material properties. A massive combinatorial phase space exists, even though it has only been partially investigated through experiments and/or simulations. High entropy alloys, for instance, are formed by mixing multiple principal components with a tendency to form unary, binary, or more complex phases that may also undergo phase transformations depending on additional processing. The main challenge concerns the high-dimensional and scattered nature of the parameter space associated with multi-component materials, making the design problem intractable based on conventional materials science approaches. It is indeed a fact that only very few of these features can be systematically explored solely based on theory, experiments, or computations, and thus, prediction of complex materials behavior has been limited. Materials data scientists make routine use of advanced statistical learning elements (i.e., regression, classification, regularization, dimensionality reduction, and cross-validation ) for insights into inherent patterns, underlying physics, and structure-property correlations across a broad range of length-scales and time-scales. In this framework, ML has been proven to be an efficient and powerful computational tool in micro-structural alloy tailoring and characterization, with robust predictions of fundamental thermomechanical and physical properties, and systematic development of improved atomic potentials based on high-throughput material computations. An emerging category pertains to deformation and failure in metal alloys owing to an ever-increasing demand for materials that may withstand extreme conditions (temperature, pressure, loading rate), and also form optimal combinations of material weight, strength, ductility, corrosion-resistance, and toughness as essential infrastructural and industrial components.

The Materials Research Society bulletin provides an exhaustive list across almost all aspects of materials science and engineering that are greatly influenced by this relatively new field, including (but not limited to) thermoelectric materials, superconductors, novel bio-materials, batteries, fuel cells, dielectric elastomers and so on. Materials informatics (MI) is an interdisciplinary field of study at the interface of material engineering and data science, which aims to boost discovery, characterization, component design, synthesis, and screening of new or alternative materials based on modern state-of-the-art machine learning (ML) techniques.

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