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Institute for Advanced Simulation (IAS)

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Research

The SLQM carries on several research projects in three distinct categories:

  • Development and maintenance of numerical libraries tailored to computational tasks emerging from Materials Science simulation software;
  • Design and implementation of high-performance algorithms targeting specific simulation codes in the broad area of quantum materials;
  • Development of new mathematical and computational models aimed at improving performance, accuracy and scalability of simulations within a methodological framework.

Projects

Robust methods for achieving self-consistency in large-scale ab-initio quantum mechanical calculations
Density Functional Theory is a powerful theory to calculate quantum mechanical properties of materials. In metallic systems of larger size, though, charge fluctuates between regions within the metal (known as charge sloshing) and the number of iterations necessary to achieve convergence, if it converges at all, increases dramatically. As part of this project we develop and implement preconditioners that extend the existing mixing models, ensure convergence and improve the its total rate.
Contact: Miriam Hinzen
(funded by JARA-HPC)

Inverse design of functional hetero-interfaces in photovoltaic applications
The PVnegf code, recently developed at the Forschungszentrum Jülich IEK-5 institute is at the base of this project. The first objective is to improve PVnegf flexibility and accuracy by introducing several mathematical and algorithmic optimizations. Concurrently the plan is to blueprint a number of design principles targeting specific features for a given desired functionality. The end result of the project will be the identification of core features opening a path towards the surgical design of complex hetero-interfaces which can then be compared against the outcome of experimental tests.
Contact: Sebastian Achilles

Optimized Solver for Sequences of Sparse Eigenvalue Problems arising in ab initio Computations
DFT simulations yield a sequence of eigenvalue problems, where only a small part of the spectrum is required. Spectrum-slicing eigensolvers promise an additional level of parallelism over more traditional solvers. Research efforts include the optimization of rational filter functions for accelerating subspace iteration. Future research will include slice placement for better performance and load-balancing.
Contact: Jan Winkelmann

Juelich Numerical Library (JuNLib)
In Materials Science, like in many other scientific domains making use of scientific computing simulations, numerical libraries play a fundamental role. SLQM develops and implements modern algorithms into numerical linear algebra libraries tailored to heterogeneous computing architectures and modular supercomputers.
Contact: Edoardo Di Napoli

Joint Laboratory on Extreme Scale Computing (JLESC) projects
There are two projects under the JLESC label. Both projects deal with the customization and integration of the Chebyshev Accelerated Subspace Eigensolver (ChASE), recently developed at JSC, into specific application codes. The aim is to facilitate the computation of the desired lowest eigenpairs of large dense and sparse eigenproblems on hybrid architectures.
Contact: Edoardo Di Napoli

Applying Machine Learning methods to Lathanides orthophosphates
Orthophosphates of lanthanides (LnPO4) are particularly interesting for their applications in forming ceramics matrices that are used in the nuclear waste management to store safely large amounts of radioactive material. One of the most important thermodynamic features of composite materials are the enthalpies of formation. Such property are not quite easy to measure especially for compounds with high-temperature melting points. We plan to retrieve enthalpies of formation using Machine Learning techniques on the available set of data for lathanides orthophosphates in their pure phases.
Contact: Edoardo Di Napoli


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