Arthur Lin^1*, Kevin Huguenin-Dumittan^2*, Yong-Cheol Cho^1*, Jigyasa Nigam^2*, Rose Cersonsky^1*

1 University of Wisconsin - Madison, Madison, Wisconsin, USA

2 Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

* Corresponding authors emails: alin62@wisc.edu, kevin.huguenin-dumittan@epfl.ch, yongcheol0820114@gmail.com, jigyasa.nigam@epfl.ch, rose.cersonsky@wisc.edu

DOI10.24435/materialscloud:mk-vn [version v1]

Publication date: Aug 14, 2024

How to cite this record

Arthur Lin, Kevin Huguenin-Dumittan, Yong-Cheol Cho, Jigyasa Nigam, Rose Cersonsky, Expanding density-correlation machine learning representations for anisotropic coarse-grained particles, Materials Cloud Archive 2024.123 (2024), https://doi.org/10.24435/materialscloud:mk-vn

Description

This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology. The three datasets are: - Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter - Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies - Crystalline configurations of planar benzene molecules, with energetics computed using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al., Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.

Materials Cloud sections using this data

Files

File name	Size	Description
liquid_crystals.zip MD5md5:303aa15186019bafa5f8544159d3b738	5.0 MiB	Zip file containing all code and data necessary for analyzing liquid crystal systems.
gay-berne.zip MD5md5:29d18ceedb2fef02ce2cdc01a23d12df	968.7 KiB	Zip file containing all code and data necessary for analyzing Gay-Berne ellipsoidal dimers.
benzenes.zip MD5md5:fed9b6816bd9570c1a93b5261c209ea1	3.9 MiB	Zip file containing all code and data necessary for analyzing crystalline benzene configurations.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning Quantum ESPRESSO benzene liquid crystals methods