In general, data-driven approaches aim to identify objectively (relying largely on the available data) the embedded correlations among selected inputs and outputs needed to study or model a given phenomenon. Data-driven approaches are particularly advantageous when a full, physics-based understanding of relevant phenomena is lacking, and also when gathering new experimental data is particularly slow and costly. The relatively recent fourth paradigm of data-driven materials science has been or is being realized in various areas of materials science. Efforts in this paradigm are mainly focused on extracting high-value information from all available materials data (generated by either experiments or computations) and expressing it as high-value linkages among material processes, structures, and properties. This is especially well suited for practical materials-design explorations. The central impediment in this effort arises from the lack of a rigorous mathematical framework for quantifying the material structure, whose salient features span multiple material length scales (from the atomistic to the macroscale). The very large number of parameters needed to fully capture all details of the hierarchical material structure make the structure representation inherently high dimensional. However, from a practical viewpoint, it is essential to be able to identify high-value, low-dimensional representations of the material structure that can be employed reliably to drive the material innovation efforts leading to enhanced properties. One type of data-driven method that has already made inroads across many subfields of materials research is that of machine learning (ML). Below, we briefly discuss examples of well-established use cases for ML that have been fruitful in areas of materials science.
Spear, Ashley D., Surya R. Kalidindi, Bryce Meredig, Antonios Kontsos, and Jean-Briac le Graverend. “Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior.” JOM 70, no. 7 (July 14, 2018): 1143–46. https://doi.org/10.1007/s11837-018-2894-0.