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Title: Machine Learning–Assisted Design of Material Properties
Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.  more » « less
Award ID(s):
1720595
NSF-PAR ID:
10434640
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Annual Review of Chemical and Biomolecular Engineering
Volume:
13
Issue:
1
ISSN:
1947-5438
Page Range / eLocation ID:
235 to 254
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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