Expanding density-correlation machine learning representations for anisotropic coarse-grained particles
Creators
- 1. University of Wisconsin - Madison, Madison, Wisconsin, USA
- 2. Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
* Contact person
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.
Files
File preview
files_description.md
All files
References
Journal reference (Paper in which the method is described and the data is discussed.) A. Y. Lin, K. K. Huguenin-Dumittan, Y.-C. Cho, J. Nigam, R. K. Cersonsky, J. Chem. Phys. 161 (2024), doi: 10.1063/5.0210910