data science and cyberinfrastructure: critical enablers ... ?· data science and...
Post on 15-Mar-2019
Embed Size (px)
Data science and cyberinfrastructure: criticalenablers for accelerated development ofhierarchical materials
Surya R. Kalidindi*
The slow pace of new/improved materials development and deployment has been identified as
the main bottleneck in the innovation cycles of most emerging technologies. Much of the
continuing discussion in the materials development community is therefore focused on the
creation of novel materials innovation ecosystems designed to dramatically accelerate materials
development efforts, while lowering the overall cost involved. In this paper, it is argued that the
recent advances in data science can be leveraged suitably to address this challenge by
effectively mediating between the seemingly disparate, inherently uncertain, multiscale and
multimodal measurements and computations involved in the current materials development
efforts. Proper utilisation of modern data science in the materials development efforts can lead to
a new generation of data-driven decision support tools for guiding effort investment (for both
measurements and computations) at various stages of the materials development. It should also
be recognised that the success of such ecosystems is predicated on the creation and utilisation
of integration platforms for promoting intimate, synchronous collaborations between cross-
disciplinary and distributed team members (i.e. cyberinfrastructure). Indeed, data sciences and
cyberinfrastructure form the two main pillars of the emerging new discipline broadly referred to as
materials informatics (MI). This paper provides a summary of current capabilities in this emerging
new field as they relate to the accelerated development of advanced hierarchical materials (the
internal structure plays a dominant role in controlling overall properties/performance in these
materials) and identifies specific directions of research that offer the most promising avenues.
Keywords: Materials informatics, Microstructure quantification, Processstructureproperty linkages, Data science, Cyberinfrastructure, Metamodels,Spatial correlations, Reduced-order representations
Materials, Manufacturing, andInformaticsMaterials with enhanced performance characteristicshave served as critical enablers for the successfuldevelopment of advanced technologies throughouthuman history and have contributed immensely to theprosperity and wellbeing of various nations. A majorityof the materials employed in advanced technologiesexhibit hierarchical internal structures with rich detailsat multiple length and/or structure scales (spanning fromatomic to macroscale). Collectively, these features of thematerial internal structure are here simply referred to asthe structure and constitute the central consideration inthe development of new/improved hierarchical materi-als. Indeed, the existence of a causal relationshipbetween the material structure and its properties is the
central tenet in the field of materials science andengineering. It should be noted that the word structureis used very broadly in these statements (and in thispaper) to include and refer to any of the details of thematerial internal structure (spanning all relevant lengthor structure scales involved).
Indeed, the mathematical description of the materialinternal structure in its entirety, in any selected materialsystem, is unimaginably complex and demands very highdimensional representation. For example, most materi-als being explored for structural applications (e.g. Tialloys in jet engines and advanced high strength steels, Mgalloys in lightweight automobiles, Al alloys in aerospaceframes, and Zr alloys in nuclear industry) exhibitpolycrystalline microstructures at the mesoscale.14 Asan example, Fig. 1 shows details of the mesoscale stru-cture in such materials. A rigorous representation of thehierarchical structure in such materials should also includedetails at other relevant length/structure scales (e.g. pointdefects, dislocations, grain boundaries, phase boundaries).Although the above discussion was framed in the context
Georgia Institute of Technology, Atlanta, GA 30332, USA
*Corresponding author, email firstname.lastname@example.org
2015 Institute of Materials, Minerals and Mining and ASM InternationalPublished by Maney for the Institute and ASM InternationalDOI 10.1179/1743280414Y.0000000043 International Materials Reviews 2015 VOL 60 NO 3150
of a crystalline material, similar considerations exist inmost other material classes. For example, the hierarchy inpolymer structures5 includes details of monomers andtheir spatial arrangements into blocks and branches at themolecular or macromolecular level, micro-fibrils andcrystallites at the nanoscale, and spherulites at the micro-scale. The hierarchy in most biological materials is indeedmuch richer. For example, the hierarchy in bone structureincludes details of collagen molecules and mineral crystals,collagen fibrils, collagen fibre, lamella, osteons and ma-crostructure (e.g. cancellous or cortical).68 Furthermore,most materials of interest in advanced technologiesactually tend to be composites comprising multiplematerial classes.
It is emphasised again that the discussion in this paperis exclusively focused on hierarchical materials. In otherwords, the simplest of these materials exhibits at leasttwo distinct well separated length or structure scales (e.g.the macroscale and the microscale). It should also benoted that the description of the structure in suchhierarchical materials implicitly includes a full descrip-tion of the chemical compositions of all distinctmicroscale constituents (called local states) present inthe material system, in addition to their relative spatialplacement in the internal structure. In other words, theinformation included in the description of the materialstructure is orders of magnitude more detailed than thesimple overall chemical composition typically used toidentify or label a material system.
Based on the above description, it should be clear thata vast number of tiered spatial distributions have to bequantified to faithfully represent the complex hierarch-ical structure of advanced material systems. It is obviousthat such an effort would result in an extremely largeand unwieldy representation. Fortunately, the field ofmaterials science and engineering has already empiri-cally discovered that only certain salient features of the
material structure dominate the macroscale performancecharacteristics of interest for any selected application.Therefore, the main challenge in the development ofmaterials with enhanced properties reduces to identify-ing and tracking the salient structure features that areimportant to a specific engineering or technologyapplication. In other words, the core knowledge neededto guide the materials development efforts can besought and expressed as reduced-order processstruc-tureproperty (PSP) linkages that capture the roles ofdifferent unit manufacturing (or processing) steps on thesalient structure features that control the propertycombinations (or performance characteristics) of inter-est. It is important to recognise that these linkagesrepresent reduced-order models as they utilise reduced-order representations of the material structure. Histori-cally, such efforts have been largely guided by thescientific approach that entails formulating a funda-mental hypothesis and then validating it with carefullydesigned experiments conducted in highly controlledenvironments. Such science-driven approaches for esta-blishing PSP linkages have been expensive and slow,911
because their focus has been to isolate and study eachphysical mechanism (i.e. cause) and its associated effectin a highly systematic manner.
From a data science perspective, one can formalise thediscussion above in terms of the fundamental datatransformations involved, as summarised in Fig. 2. Rawdata related to materials phenomena of interest is usuallygenerated by some combination of experiments, models,and simulations. Recent years have witnessed an explosionin the ability of materials experts to generate data fromnovel experiments and simulations. For example, the 3-Dexperimental dataset shown in Fig. 1 can now be generatedusing mostly automated protocols.12,13 In spite of thisautomation, this technique incurs a substantial amount oftime (of the order of several days). An exciting developmentin this field is the use of a femto-second laser for fast serialsectioning of the sample,14 as opposed to the conventionalmechanical approaches used in the earlier studies. This newtechnique has the potential to dramatically reduce the timerequired to obtain a 3-D structure dataset. It has also beendemonstrated that a focused ion beam attached to ascanning electron microscope can be used for serialsectioning the samples and reconstructing a 3-D materialstructure dataset (e.g. Refs. 15 and 16). However, thistechnique is ideal only for studies of very small volumesof material (with length scales of the order of a fewmicrometres). While the approaches mentioned earlier areall destructive (they ablate the material to expose newsurfaces of the sample), there are also a number of non-destructive techniques that rely on the use of X-rays. Whenthe X-ray techniques are combined with computedtomography techniques, it is possible to produce recon-structions of a broad range of 3-D material datasetsincluding porous structures (e.g. Refs. 1719), mapping ofdefects (e.g. Refs. 2022) and polycrystal microstructures(e.g. Refs. 23 and 24). In fact, it is now possible to obtain 4-D (three spatial dimensions and time) reconstructions usingdata gathered from high energy X-rays.25 At the finestspatial resolution, it is also possible to obtain 3-D and 4-Dstructu