Recent advances in machine learning towards multiscale soft materials design

Nicholas E. Jackson, Michael A. Webb, Juan J. de Pablo

Research output: Contribution to journalReview articlepeer-review

21 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)106-114
Number of pages9
JournalCurrent Opinion in Chemical Engineering
Volume23
DOIs
StatePublished - Mar 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Energy(all)

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