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Inverse design of self-assembling Frank-Kasper phases and insights into emergent quasicrystals

05 December 2017, Version 1
Working Paper
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We discuss how a machine learning approach based on relative entropy optimization can be used as an inverse design strategy to discover isotropic pair interactions that self-assemble single- or multi-component particle systems into Frank-Kasper phases. In doing so, we also gain insights into self-assembly of quasicrystals.

Keywords

inverse design
relative entropy
Frank-Kasper phases
quasicrystals
machine learning
Chemistry

Supplementary materials

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Now Published

Inverse Design of Self-Assembling Frank-Kasper Phases and Insights Into Emergent Quasicrystals [opens in a new tab]
Beth A. Lindquist, Ryan B. Jadrich, William D. Piñeros, Thomas M. Truskett journal article
The Journal of Physical Chemistry B , Volume 122, Issue 21
Online publication date: Feb 27, 2018

Version History

Dec 05, 2017 Version 1

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License

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The content is available under CC BY NC ND 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.5660545.v1
D O I: 10.26434/chemrxiv.5660545.v1 [opens in a new tab]

Funding

NSF, Welch

Author’s competing interest statement

no conflict of interest

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