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Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials
Achievement/Results
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.
