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SpaGrOW — A Derivative-Free Optimization Scheme for Intermolecular Force Field Parameters Based on Sparse Grid Methods

  • Molecular modeling is an important subdomain in the field of computational modeling, regarding both scientific and industrial applications. This is because computer simulations on a molecular level are a virtuous instrument to study the impact of microscopic on macroscopic phenomena. Accurate molecular models are indispensable for such simulations in order to predict physical target observables, like density, pressure, diffusion coefficients or energetic properties, quantitatively over a wide range of temperatures. Thereby, molecular interactions are described mathematically by force fields. The mathematical description includes parameters for both intramolecular and intermolecular interactions. While intramolecular force field parameters can be determined by quantum mechanics, the parameterization of the intermolecular part is often tedious. Recently, an empirical procedure, based on the minimization of a loss function between simulated and experimental physical properties, was published by the authors. Thereby, efficient gradient-based numerical optimization algorithms were used. However, empirical force field optimization is inhibited by the two following central issues appearing in molecular simulations: firstly, they are extremely time-consuming, even on modern and high-performance computer clusters, and secondly, simulation data is affected by statistical noise. The latter provokes the fact that an accurate computation of gradients or Hessians is nearly impossible close to a local or global minimum, mainly because the loss function is flat. Therefore, the question arises of whether to apply a derivative-free method approximating the loss function by an appropriate model function. In this paper, a new Sparse Grid-based Optimization Workflow (SpaGrOW) is presented, which accomplishes this task robustly and, at the same time, keeps the number of time-consuming simulations relatively small. This is achieved by an efficient sampling procedure for the approximation based on sparse grids, which is described in full detail: in order to counteract the fact that sparse grids are fully occupied on their boundaries, a mathematical transformation is applied to generate homogeneous Dirichlet boundary conditions. As the main drawback of sparse grids methods is the assumption that the function to be modeled exhibits certain smoothness properties, it has to be approximated by smooth functions first. Radial basis functions turned out to be very suitable to solve this task. The smoothing procedure and the subsequent interpolation on sparse grids are performed within sufficiently large compact trust regions of the parameter space. It is shown and explained how the combination of the three ingredients leads to a new efficient derivative-free algorithm, which has the additional advantage that it is capable of reducing the overall number of simulations by a factor of about two in comparison to gradient-based optimization methods. At the same time, the robustness with respect to statistical noise is maintained. This assertion is proven by both theoretical considerations and practical evaluations for molecular simulations on chemical example substances.

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Metadaten
Document Type:Article
Language:English
Author:Marco Hülsmann, Dirk Reith
Parent Title (English):Entropy
Volume:15
Issue:9
First Page:3640
Last Page:3687
ISSN:1099-4300
URN:urn:nbn:de:hbz:1044-opus-10640
DOI:https://doi.org/10.3390/e15093640
Publisher:MDPI
Place of publication:Basel
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2013/09/06
Copyright:(c) 2013 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license.
Keyword:atomistic models; derivative-free optimization; force field parameterization; molecular simulations; smoothing procedures; sparse grids
Departments, institutes and facilities:Fachbereich Ingenieurwissenschaften und Kommunikation
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Projects:Verbundprojekt SkaSim: Skalierbare HPC-Software für molekulare Simulationen in der chemischen Industrie (DE/BMBF/01IH13005B)
Dewey Decimal Classification (DDC):6 Technik, Medizin, angewandte Wissenschaften / 66 Chemische Verfahrenstechnik / 660 Chemische Verfahrenstechnik
Entry in this database:2015/04/02
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 3.0