Multi-scale modeling of complex materials
The research activities of the Sierka group focus on the development and applications of multi-scale computational methods for investigating structure, properties and reactivity of complex materials – nanoparticles, thin films, surfaces and interfaces. Many chemical and physical properties of these materials arise from processes and features at multiple scales, both spatial and temporal. Therefore, our work involves simulations of material properties using information or models from different levels of theory: quantum mechanics, molecular mechanics and dynamics as well as mesoscale levels.
The spectrum of the methods currently developed in the group ranges from quantum chemical methods for extended systems to global structure optimization algorithms and methods based on molecular dynamics. These methodological developments are applied within research projects conducted in close collaboration with experimental groups from different disciplines of chemistry and physics.
Simulation methods for large molecules, surfaces and solids
The basis for our research projects within this area is the TURBOMOLE quantum chemical program package, initially developed in the group of Reinhart Ahlrichs at the University of Karlsruhe and at the Forschungszentrum Karlsruhe. With almost 20 years of continuous development TURBOMOLE has become a valuable tool used by academic and industrial researchers. It is used in research areas ranging form materials science, inorganic and organic chemistry to various types of spectroscopy, and biochemistry.
The research of the group in this area is devoted to the extension of the methods available within the TURBOMOLE program to periodic systems such as surfaces, interfaces and bulk solids.
Our recent publications within this project are:
- Density functional theory for molecular and periodic systems using density fitting and continuous fast multipole method: Analytical gradients: J. Comput. Chem. 2016, in print.
- Low-memory iterative density fitting: J. Comput. Chem. 2015, 36, 1521–1535.
Global structure optimization methods
Our research within this area is devoted to the development of global optimization methods and their application for design of novel materials. In general, efficient structure optimization methods are important prerequisite for computational studies of structure and properties of materials. Local optimization methods locate the nearest local minimum or a saddle point and need a reasonable initial starting point. Global optimization methods are able to locate the global energy minimum independent of the initial structure. Therefore, such methods are well suited for the design of novel materials and for structure determination of systems, which are difficult to access experimentally. The DoDo program package developed within this project uses genetic algorithm (GA) as the global optimization method. It proved efficient for automatic structure resolution of both molecular systems, surfaces and interfaces. The current application area within this project is the design and structure determination of novel low-dimensional materials by a combination of calculations and experiments.
Our recent publications within this project are:
- Metal-Supported Oxide Nanofilms, In: Computational Modelling of Inorganic Nanomaterials; S. T. Bromley and M. Zwijnenburg (Eds); CRC Press, 2016, 335–367.
- CdO and ZnO Clusters as Potential Building Blocks for Cluster-Assembled Materials: A Combined Experimental and Theoretical Study: J. Phys. Chem. C 2015, 119, 6886–6895.
In silico design of multifunctional polymers
Achieving optimal solubility of active substances in polymeric carriers is of fundamental importance for a number of industrial applications, including targeted drug delivery within the growing field of nanomedicine. However, its experimental optimization using a trial-and-error approach is cumbersome and time-consuming. In contrast, computer simulations are not only capable of efficiently predicting the drug-polymer compatibility but also provide a detailed understanding of the underlying interactions. This knowledge can be employed as a powerful tool in the process of discovery and optimization of new drug delivery systems.
In this research area computationally efficient approaches are developed for rapid prediction of thermodynamic compatibility between active species and copolymers comprising hydrophilic and hydrophobic segments. Ultimately, this in silico approach combined with experiments will provide superior polymeric carrier materials for various drug formulations.
Our recent publications within this project are:
- Thermodynamic compatibility of actives encapsulated into PEG-PLA nanoparticles: In Silico predictions and experimental verification: J. Comput. Chem. 2016, 37, 2220–2227.
