Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Fraunhofer Institute for Algorithms and Scientific Computing SCAI

Scope of services

We offer expertise in

  • High-performance computing in quantum mechanics, molecular dynamics, and continuum mechanics
  • Mathematical and statistical methods for experiment design, uncertainty quantification, sensitivity analysis, and parameter estimation
  • Numerical methods for high-dimensional problems and multi-objective optimization

and solutions for

  • Computer-aided materials and molecular design
  • Process optimization and efficient exploration of design space
  • Materials and cheminformatics
  • Integrated computational materials engineering
  • Improvement of computational models and estimation of reliability and confidence
  • Simulation-based decision support systems
© Fraunhofer SCAI
 

Young's modulus of nanotubes

A nano-tubes‘ response to stress is accessible via Parrinello-Rahman-MD. Straining and finally even rupturing can be captured by a reactive many-body bond order Tersoff-potential.

 

Reaction pathways and kinetics

Broken down in simple steps reaction‘s energetic profile is accessible via QM-computations. Based on activation energies and reaction enthalpies the kinetics of a system can be predicted as well.

 

Ionic liquids

Combining  different organic cations and anions yields a wide range low melting salts, some with very specific properties for use as  e.g. non-volatile lubricants. The search for the best combination can be supported by CAMD-techniques ranging from simulations to machine-learning.

Membrane permeability

Whether or not a substance may enter a cell, depends on its free energy profile when passing through the cell‘s membrane. This can be recorded in  MD simulations and relates to the compound‘s toxic potential.

Physical properties

A compound‘s typical key figures in good accuracy are accessible from Molecular Dynamics and/or Monte-Carlo-Simulations.

Selected references

  1. J. Schneider, J. Hamaekers, S. T Chill, S. Smidstrup, J. Bulin, R. Thesen, A. Blom and K. Stokbro.  ATK-ForceField: a new generation molecular dynamics software package. Modelling  Simul. Mater. Sci.Eng.  25, 8:085007, 2017.
  2. M. Griebel, J. Hamaekers, and R. Chinnamsetty. An Adaptive Multiscale Approach for Electronic Structure Methods. Multiscale Modeling & Simulation, 16(2):752-776, 2018.
  3. J. Barker, J. Bulin, J. Hamaekers, and S. Mathias. LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential, pages 25-42. Springer International Publishing, Cham, 2017.
  4. J. Barker, G. Bollerhey, and J. Hamaekers. A multilevel approach to the evolutionary generation of polycrystalline structures. Computational Materials Science, 114:54-63, 2016.
  5. C.Diedrich, D. Dijkstra, J. Hamaekers, B. Henninger, and M. Randrianarivony. A finite element study on the effect of curvature on the reinforcement of matrices by randomly distributed and curved nanotubes. Journal of Computational and Theoretical Nanoscience, 12:2108-2116, 2015.
  6. M. Griebel, S. Knapek, and G. Zumbusch. Numerical Simulation in Molecular Dynamics. Springer, Berlin, Heidelberg, 2007.
  7. M. Griebel, S. Knapek, G. Zumbusch, and A. Caglar. Numerische Simulation in der Moleküldynamik. Numerik, Algorithmen, Parallelisierung, Anwendungen. Springer, Berlin, Heidelberg, 2003.
  8. J. Barker, G. Bollerhey, and J. Hamaekers. A multilevel approach to the evolutionary generation of polycrystalline structures. Computational Materials Science, 114:54-63, 2016.
  9. C. Diedrich, D. Dijkstra, J. Hamaekers, B. Henninger, and M. Randrianarivony. A finite element study on the effect of curvature on the reinforcement of matrices by randomly distributed and curved nanotubes. Journal of Computational and Theoretical Nanoscience, 12:2108-2116, 2015.
  10. M. Griebel and J. Hamaekers. Fast discrete Fourier transform on generalized sparse grids. In Sparse grids and Applications, volume 97 of Lecture Notes in Computational Science and Engineering, pages 75-108. Springer, 2014.
  11. C. Neuen, M. Griebel, and J. Hamaekers. Multiscale simulation of ion migration for battery systems. MRS Online Proceedings Library, 1535, 2013.
  12. J. S. Dolado, M. Griebel, J. Hamaekers, and F. Heber. The nano-branched structure of cementitious calcium-silicate-hydrate gel. Journal of Materials Chemistry, 21:4445-4449, 2011.
  13. J. S. Dolado, M. Griebel, and J. Hamaekers. A molecular dynamics study of cementitious silicate hydrate (C-S-H) gels. Journal of the American Ceramic Society, 90(12):3938-3942, 2007.
  14. M. Griebel and J. Hamaekers. Molecular dynamics simulations of boron-nitride nanotubes embedded in amorphous Si-B-N. Computational Materials Science, 39(3):502-517, 2007.
  15. M. Griebel and J. Hamaekers. Molecular dynamics simulations of the elastic moduli of polymer-carbon nanotube composites. Computer Methods in Applied Mechanics and Engineering, 193(17-20):1773-1788, 2004.
  16. S. J. V. Frankland, A. Caglar, D. W. Brenner, and M. Griebel. Molecular simulation of the influence of chemical cross-links on the shear strength of carbon nanotube - polymer interfaces. Journal of Physical Chemistry B, 106(12):3046-3048, 2002.

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