Dr. Sebastian Matera

Head of ECMath-Junior Research Group Artificial Photosynthesis/Scientific Computing

Institut f. Mathematik, Freie Universität Berlin
Arnimallee 9
14195 Berlin
+49 (0) 30 838 75625

Research focus

Scientific Computing/Numerics
Multiscale Modeling
Stochastic Modeling and Simulation

Projects as a project leader

  • SE23

    Multilevel adaptive sparse grids for parametric stochastic simulation models of charge transport

    Dr. Sebastian Matera

    Project heads: Dr. Sebastian Matera
    Project members: -
    Duration: 01.06.2017 - 31.12.2018
    Status: running
    Located at: Freie Universität Berlin


    Many computational models are stochastic and the model output needs require some sort of sampling. Besides this intrinsic stochasticity, the models usually depend on a number of uncertain parameters. We develop a multi-level adaptive sparse grid strategy to address this parametric uncertainty, where the sampling effort is adjusted to the level of the sparse grid. This methodology is applied to stochastic simulation models of charge transport, as they appear in photovoltaics and photocatalysis.

  • SE14

    Error-aware analysis of multi-scale reactivity models for photochemical surface reactions

    Dr. Sebastian Matera

    Project heads: Dr. Sebastian Matera
    Project members: Sandra Doepking
    Duration: -
    Status: completed
    Located at: Freie Universität Berlin


    On the route to a more efficient exploitation of energy and materials, the design of new heterogenous catalysts is a central aspect, be it for the production of fine chemicals or the conversion of solar energy to fuels. Ideally, such a design is based on an atomistic understanding of the origin of catalytic activity. In order to enable this detailed undestanding, first-principles kinetic Monte Carlo approaches have been established during the last decade. Despite their success, these still have some some limitations. On the one hand, the electronic structure methods, employed to determine required rate parameters, introduce a non-negligible error into the later. On the other hand, the need for stochastic simulation and the typically large dimension of the parameter space hampers the determination of the rate determining steps by sensitivity analysis, i.e. the most interesting input for a rational catalyst design. The purpose of this project is to address the aforementioned problems by development of tools for local and global sensitivity analyses. These are applied to models from classical catalysis, but also to models for photo-catalytic processes.