A Bayesian framework for materials knowledge systems
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rtificial Intelligence Prospective
A Bayesian framework for materials knowledge systems Surya R. Kalidindi
, Georgia Institute of Technology, Atlanta, GA, USA
Address all correspondence to Surya R. Kalidindi at [email protected] (Received 7 January 2019; accepted 23 April 2019)
Abstract This prospective offers a new Bayesian framework that could guide the systematic application of the emerging toolsets of machine learning in the efforts to address two of the central bottlenecks encountered in materials innovation: (i) the capture of core materials knowledge in reduced-order forms that allow one to rapidly explore the vast materials design spaces, and (ii) objective guidance in the selection of experiments or simulations needed to identify the governing physics in the materials phenomena of interest. The framework builds on recent advances in the low-dimensional representation of the statistics describing the material’s hierarchical structure.
Introduction The announcement of the Materials Genome Initiative (MGI)[1] as a national strategic initiative has spurred research explorations in completely new cross-disciplinary directions that are poised to cut across conventional disciplines such as materials science, manufacturing science, mechanics and design, and the emerging disciplines such as data sciences and informatics.[2–10] One of the important outcomes from these explorations is the increased recognition and appreciation of the critical role of data sciences and informatics, in conjunction with experimental and modeling sciences, in supporting the MGI vision for accelerated materials innovation. It is now becoming clear that materials data sciences and informatics can support the MGI vision in at least two ways. First, materials data sciences can offer novel toolsets for maximizing the re-usable knowledge mined from the large experimental and modeling datasets produced by materials researchers and specialists. This is particularly important because the past few decades have witnessed tremendous advances in both physics-based multiscale materials modeling[11] and multi-resolution multiscale materials characterization (e.g., Refs. 12–15), which have dramatically increased the volume and velocity of materials data. Since the advances in experimental and computational materials sciences are expected to further intensify in coming years, there is a critical need to support these advances with suitable data analytic tools. Only through the proper use of materials data analytic tools we will be able to systematically and comprehensively analyze very large datasets produced by materials researchers, mine the embedded knowledge in the previously aggregated datasets, and objectively guide future effort while increasing efficiency and productivity. Second, materials informatics can play a critical role in facilitating intimate and seamless communications between the diverse stakeholders of the materials
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innovation community. These communications deal with the exchange of high-value knowledge and exper
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