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We report computer simulations of two-dimensional convex hard superellipse particle phases vs. particle shape parameters including aspect ratio, corner curvature, and sidewall curvature. Shapes investigated include disks, ellipses, squares, rectangles, and rhombuses, as well as shapes with non-uniform curvature including rounded squares, rounded rectangles, and rounded rhombuses. Using measures of orientational order, order parameters, and a novel stretched bond orientational order parameter, we systematically identify particle shape properties that determine liquid crystal and crystalline phases including their coarse boundaries and symmetry. We observe phases including isotropic, nematic, tetratic, plastic crystals, square crystals, and hexagonal crystals (including stretched variants). Our results catalog known benchmark shapes, but include new shapes that also interpolate between known shapes. Our results indicate design rules for particle shapes that determine two-dimensional liquid, liquid crystalline, and crystalline microstructures that can be realized via particle assembly.
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Machine learning approaches for the optimization of packing densities in granular matter
We discuss how machine learning methods can support the search for optimally dense packing shapes in a high-dimensional shape space. Using dimensional reduction, regression, and numerical optimization we find novel shapes that pack with up to 0.733 volume fraction.
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- Award ID(s):
- 1945909
- PAR ID:
- 10493557
- Publisher / Repository:
- Soft Matter
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 19
- Issue:
- 36
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 6875 to 6884
- Subject(s) / Keyword(s):
- machine learning granular matter packings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2SM01430K
