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Predicting crystal growth via a unified kinetic three-dimensional partition model

Abstract

Understanding and predicting crystal growth is fundamental to the control of functionality in modern materials. Despite investigations for more than one hundred years, it is only recently that the molecular intricacies of these processes have been revealed by scanning probe microscopy. To organize and understand this large amount of new information, new rules for crystal growth need to be developed and tested. However, because of the complexity and variety of different crystal systems, attempts to understand crystal growth in detail have so far relied on developing models that are usually applicable to only one system9,10,11. Such models cannot be used to achieve the wide scope of understanding that is required to create a unified model across crystal types and crystal structures. Here we describe a general approach to understanding and, in theory, predicting the growth of a wide range of crystal types, including the incorporation of defect structures, by simultaneous molecular-scale simulation of crystal habit and surface topology using a unified kinetic three-dimensional partition model. This entails dividing the structure into ‘natural tiles’ or Voronoi polyhedra that are metastable and, consequently, temporally persistent. As such, these units are then suitable for re-construction of the crystal via a Monte Carlo algorithm. We demonstrate our approach by predicting the crystal growth of a diverse set of crystal types, including zeolites, metal–organic frameworks, calcite, urea and L-cystine.

Category

Academic article

Client

  • Research Council of Norway (RCN) / 233848

Language

English

Author(s)

  • Michael W Anderson
  • James T. Gebbie-Rayet
  • Adam R. Hill
  • Nani Farida
  • Martin P. Attfield
  • Pablo Cubillas
  • Vladislav A. Blatov
  • Davide M. Proserpio
  • Duncan Akporiaye
  • Bjørnar Arstad
  • Julian D. Gale

Affiliation

  • University of Manchester
  • Samara National Research University named after academic S.P. Korolyov
  • China
  • University of Milan
  • SINTEF Industry / Process Technology
  • Curtin University

Date

27.04.2017

Year

2017

Published in

Nature

ISSN

0028-0836

Volume

544

Issue

7651

Page(s)

456 - 459

View this publication at Cristin