Classifying crystals of rounded tetrahedra and determining their order parameters using dimensionality reduction

Robin Van Damme*, Gabriele M. Coli, Rene Van Roij, Marjolein Dijkstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Using simulations we study the phase behavior of a family of hard spherotetrahedra, a shape that interpolates between tetrahedra and spheres. We identify 13 close-packed structures, some with packings that are significantly denser than previously reported. Twelve of these are crystals with unit cells of N = 2 or N = 4 particles, but in the shape regime of slightly rounded tetrahedra we find that the densest structure is a quasicrystal approximant with a unit cell of N = 82 particles. All 13 structures are also stable below close packing, together with an additional 14th plastic crystal phase at the sphere side of the phase diagram, and upon sufficient dilution to packing fractions below 50-60% all structures melt. Interestingly, however, upon compressing the fluid phase, self-assembly takes place spontaneously only at the tetrahedron and the sphere side of the family but not in an intermediate regime of tetrahedra with rounded edges. We describe the local environment of each particle by a set of l-fold bond orientational order parameters ql, which we use in an extensive principal component analysis. We find that the total packing fraction as well as several particular linear combinations of ql rather than individual ql’s are optimally distinctive, specifically the differences q4 - q6 for separating tetragonal from hexagonal structures and q4-q8 for distinguishing tetragonal structures. We argue that these characteristic combinations are also useful as reliable order parameters in nucleation studies, enhanced sampling techniques, or inverse-design methods involving odd-shaped particles in general.

Original languageEnglish
Pages (from-to)15144-15153
Number of pages10
JournalACS Nano
Volume14
Issue number11
DOIs
Publication statusPublished - 24 Nov 2020

Funding

This work is part of the D-ITP consortium, a program of The Netherlands Organisation for Scientific Research (NWO) that is funded by the Dutch Ministry of Education, Culture and Science (OCW). We acknowledge financial support from an NWO-VICI grant.

Keywords

  • Computer simulation
  • Dimensionality reduction
  • Machine learning
  • Nanoparticles
  • Quasicrystal
  • Self-assembly
  • Tetrahedra

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