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A Bayesian framework for materials knowledge systems

Published online by Cambridge University Press:  07 May 2019

Surya R. Kalidindi Open the ORCID record for Surya R. Kalidindi [Opens in a new window]
Surya R. Kalidindi*
Affiliation:
Georgia Institute of Technology, Atlanta, GA, USA
*
Address all correspondence to Surya R. Kalidindi at surya.kalidindi@me.gatech.edu
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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.

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Artificial Intelligence Prospectives
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MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 518 - 531
Copyright
Copyright © Materials Research Society 2019 

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