Modeling and Computing (MC)

MC

 Modelling and Computing (MC)

In the ILMC, researchers have state-of-the-art knowledge and expertise both in modelling of molecular nanomagnets and in various types of simulations of their properties leading to understanding of experimental performance as well as in high performance computing and global optimization. 

The understanding and modeling of molecular nanomagnet properties, processing and performance. Synergy between various computational approaches: ab-initio theories, model Hamiltonian methods, Monte Carlo and Molecular Dynamics microscopic simulation tools

More objectives:

  • Balance between simulation, theory, experiment, validation and industrial applications.
  • Reinforcement of high performance computing based on shared resources and scientific grid infrastructure

MAGMANet Nodes Involved: AMU, CNR-INFM, INSTM, UAIC, UVEG

Knowledge and Expertise

In the Integrated Laboratory Modeling and Computing (ILMC), researchers have state-of-the-art knowledge and expertise both in modeling of molecular nanomagnets built up by chemists from individual molecules and in various types of simulations of their properties leading to understanding of experimental performance as well as in high performance computing and global optimization. 

The following areas are covered:

  • Interpretation of EPR and NMR spectra
  • Calculation of bulk magnetic properties and dynamics
  • Calculation of energy spectra including anisotropy effects
  • Ab-initio study of electronic structure and magnetic couplings
  • Simulation of heterometallic rings and chains
  • Optimization techniques based on genetic algorithms for industrial applications
  • Implementation of high performance computing in the distributed environment and integration of resources in the grid infrastructure

 

Areas of application

The broad knowledge base provided by this Integrated Laboratory affords transfer of organization skills, access to variety of specialized computational tools and software as well as comprehensive expertise which can address problems in the following important areas:

  • High performance scientific computing in materials science
  • Grid computing for public and commercial units
  • e-Learning management
  • Optimization tasks based on genetic algorithms
  • Artificial intelligence in bio-medicine

 

CONTACT

Grzegorz Kamieniarz, Head of ILMC
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Grzegorz Musial, Responsible at AMU
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Federico Totti, Responsible at INSTM
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Valerio Bellini, Responsible at INFM
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Juan M. Clemente, Responsible at UVEG
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Laurentiu Stoleriu, Responsible at UAIC
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Alexandru Stancu
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For further information please see the brochure. 

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