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Title: Recent advances in machine learning towards multiscale soft materials design.

Abstract

The multiscale design of soft materials requires an ensemble of computational techniques spanning quantum-chemistry to molecular dynamics to continuum modeling. The recent emergence of machine-learning (ML) and modern optimization algorithms has accelerated material property prediction, as well as stimulated the development of hybrid ML/molecular modeling methodologies capable of providing physical insights unobtainable from purely physics-based modeling and intuition. Such hybrid techniques also have important ramifications for the ML-enhanced interpretation of results from simulations and experiments alike. Leveraging ML techniques for the design of chemical or morphological structures based on a target property or functionality represents an exciting goal for the general area of soft materials, including polymers, liquid crystals, colloids, or biomolecules, to name a few representative classes of systems. Here, we provide a perspective on recent work using ML techniques of relevance for the multiscale design of soft materials and outline potential future directions of interest to the soft materials community.

Authors:
; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science - Office of Basic Energy Sciences - Materials Sciences and Engineering Division; USDOE Office of Science - Office of Basic Energy Sciences - Materials Sciences and Engineering Division - Midwest Integrated Center for Computational Materials (MICCoM)
OSTI Identifier:
1532508
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Current Opinion in Chemical Engineering
Additional Journal Information:
Journal Volume: 23; Journal Issue: SI
Country of Publication:
United States
Language:
English

Citation Formats

Jackson, Nicholas E., Webb, Michael A., and de Pablo, Juan J. Recent advances in machine learning towards multiscale soft materials design.. United States: N. p., 2019. Web. doi:10.1016/j.coche.2019.03.005.
Jackson, Nicholas E., Webb, Michael A., & de Pablo, Juan J. Recent advances in machine learning towards multiscale soft materials design.. United States. doi:10.1016/j.coche.2019.03.005.
Jackson, Nicholas E., Webb, Michael A., and de Pablo, Juan J. Fri . "Recent advances in machine learning towards multiscale soft materials design.". United States. doi:10.1016/j.coche.2019.03.005.
@article{osti_1532508,
title = {Recent advances in machine learning towards multiscale soft materials design.},
author = {Jackson, Nicholas E. and Webb, Michael A. and de Pablo, Juan J.},
abstractNote = {The multiscale design of soft materials requires an ensemble of computational techniques spanning quantum-chemistry to molecular dynamics to continuum modeling. The recent emergence of machine-learning (ML) and modern optimization algorithms has accelerated material property prediction, as well as stimulated the development of hybrid ML/molecular modeling methodologies capable of providing physical insights unobtainable from purely physics-based modeling and intuition. Such hybrid techniques also have important ramifications for the ML-enhanced interpretation of results from simulations and experiments alike. Leveraging ML techniques for the design of chemical or morphological structures based on a target property or functionality represents an exciting goal for the general area of soft materials, including polymers, liquid crystals, colloids, or biomolecules, to name a few representative classes of systems. Here, we provide a perspective on recent work using ML techniques of relevance for the multiscale design of soft materials and outline potential future directions of interest to the soft materials community.},
doi = {10.1016/j.coche.2019.03.005},
journal = {Current Opinion in Chemical Engineering},
number = SI,
volume = 23,
place = {United States},
year = {2019},
month = {3}
}