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Title: A snapshot review on soft materials assembly design utilizing machine learning methods
Since the surge of data in materials-science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular-dynamics engines such as LAMMPS and HOOMD-blue.  more » « less
Award ID(s):
2144094
PAR ID:
10508051
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Springer
Date Published:
Journal Name:
MRS Advances
ISSN:
2059-8521
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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