Maximally dense crystallographic symmetry group packings for molecular crystal structure prediction acceleration.

Torda, Miloslav ORCID: 0000-0002-2115-7811
(2023) Maximally dense crystallographic symmetry group packings for molecular crystal structure prediction acceleration. PhD thesis, University of Liverpool.

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Molecular crystal structure prediction (CSP) seeks the most stable periodic structure given the chemical composition of a molecule and pressure-temperature conditions. Modern CSP solvers use global optimization methods to search for structures with minimal free energy within a complex energy landscape induced by intermolecular potentials. A major caveat of these methods is that initial configurations are random, making thus the search susceptible to convergence at local minima. Providing initial configurations that are densely packed with respect to the geometric representation of a molecule can significantly accelerate CSP. Motivated by these observations, we define a class of periodic packings restricted to crystallographic symmetry groups (CSG) and design a search method for the densest CSG packings in an information-geometric framework. Since the CSG induce a toroidal topology on the configuration space, a non-euclidean trust region method is performed on a statistical manifold consisting of probability distributions defined on an n-dimensional flat unit torus by extending the multivariate von Mises distribution. Introducing an adaptive quantile reformulation of the fitness function into the optimization schedule provides the algorithm with a geometric characterization through local dual geodesic flows. Moreover, we examine the geometry of the adaptive selection-quantile defined trust region and show that the algorithm performs a maximization of stochastic dependence among elements of the extended multivariate von Mises distributed random vector. We experimentally evaluate its behaviour and performance on various densest packings of convex polygons in two-dimensional CSGs for which optimal solutions are known. Additionally, we demonstrate the application of the densest CSG packings in the pentacene thin-film CSP. We then employ the Entropic Trust Region Packing Algorithm to examine the densest packing configurations of $34$ regular convex polygons and the disc in $17$ wallpaper groups, determined computationally. The study reveals intriguing relationships between a wallpaper group's symmetries and the symmetries of a polygon. These results could have implications for crystallization problems in materials science and biology.

Item Type: Thesis (PhD)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 24 Aug 2023 14:46
Last Modified: 24 Aug 2023 14:47
DOI: 10.17638/03170955
  • Kurlin, Vitaliy