Role of materials data science and informatics in accelerated materials innovation
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Introduction Materials innovation initiatives A number of US-based,1–3 as well as international,4,5 efforts are now focused on accelerated deployment of advanced materials in commercial products. Currently employed protocols follow a sequential process that starts with materials discovery, systematically progressing through materials development, property optimization, systems design and integration, certification, and manufacturing, leading eventually to commercial deployment.1 This sequential workflow is intensive, both in terms of time and cost, and is generally reported to take 15–25 years.1,2,6,7 There is clearly an incentive to transform these sequential workflows to more dynamic workflows that allow for concurrent consideration and utilization of legacy as well as currently available information and knowledge from diverse stakeholders at each step of the decision-making process. Announced in June 2011, the Materials Genome Initiative (MGI) specifically identified these issues, and established the
goal of reducing the time and cost of materials development and deployment by 50%.1 Essential to achieving this goal is the development and deployment of a supporting infrastructure that integrates a wide range of data, experimental, and computational assets into materials innovation efforts. In 2014, the MGI Strategic Plan8 highlighted the need to facilitate the integration of experimental data, computational data, and theory across material classes, and to make experimental and computational data accessible, sharable, and transformable. Building this materials data infrastructure through the MGI will enable integrated computational materials engineering (ICME)2 approaches to be deployed with greater success and efficiency and enable the ultimate goals of the MGI to be achieved. The realization of the ambitious vision and goals of the initiatives described demands a revolutionary transformation in current materials innovation protocols. Numerous reports and publications in the recent literature have identified the key
Surya R. Kalidindi, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA; [email protected] David B. Brough, School of Computational Science and Engineering, Georgia Institute of Technology, USA; [email protected] Shengyen Li, National Institute of Standards and Technology, USA; [email protected] Ahmet Cecen, School of Computational Science and Engineering, Georgia Institute of Technology, USA; [email protected] Aleksandr L. Blekh, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA; [email protected] Faical Yannick P. Congo, Material Measurement Laboratory, Materials Science and Engineering Division, National Institute of Standards and Technology, USA; [email protected] Carelyn Campbell, Material Measurement Laboratory, Materials Science and Engineering Division, National Institute of Standards and Technology, USA; [email protected] doi:10.1557/mrs.2016.164
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MRS BULLETIN • VOLUME 41
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