HPC-ready functional renormalisation group methods for correlated electrons

Functional renormalisation group methods have become one of several standard methods to investigate models for correlated electron systems. In certain cases and under certain conditions the numerical implementation can highly profit from parallel HPC architectures. We develop methodical extensions and implement them in an evolving code base. Further more, we support a community of scientists in this field by providing support and assistance concerning the usage of HPC-systems, and organise regular workshops on this subject together with our partners.

Team

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quantumM.jpg

ChASE_Logo_RGB.jpg

HPC Tensors

Parallel and HPC numerical libraries

In Materials Science, numerical libraries play a fundamental role. We aim at designing numerical algorithms that can exploit knowledge extracted from Materials Science applications to increase parallel efficiency and minimize computational time. We develop the resulting algorithms and implement them into modern numerical linear algebra libraries tailored to heterogeneous computing architectures and modular supercomputers.

Green's functions methods in Materials Science

The objective is to improve the flexibility and accuracy of Green's functions methods 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 2D hetero-interfaces which can then be compared against the outcome of experimental tests.

Scientific Machine Learning in Quantum Materials

Simulation science and data science are becoming increasingly intertwined and mutually important. In the field of quantum simulations, Machine and Deep Learning techniques are becoming increasingly common. Despite this convergence between simulations and data, many aspects of learning from data are still unknown. We focus in the area of explainable Machine and Deep Learning applied to specific topics in Materials Science and Numerical Linear Algebra with the aim of further increase the scientific knowledge and accelerate its discovery.

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EoCoE - Energy oriented Center of Excellence

At the crossroads of the energy and digital revolutions, EoCoE develops and applies cutting-edge computational methods in its mission to accelerate the transition to the production, storage and management of clean, decarbonized energy. EoCoE offers a network of experts in HPC and sustainable energies to design powerful numerical simulations with unprecedented accuracy and performance.

HANAMI - HPC ALLIANCE FOR APPLICATIONS AND SUPERCOMPUTING INNOVATION: THE EUROPE - JAPAN COLLABORATION

ELution's main goal is to implement a new multidisciplinary concept to enhance annotation of metabolites by (a) integrating existing metabolomics platforms for more accurate combined annotation, and (b) supplementing mass spectral databases with synthetic mass spectra from quantum chemistry-based simulation methods. With this highly improved annotation of metabolites, we create new possibilities in data analysis and knowledge discovery.

Joint Lab Virtual Materials Design

Virtual materials development is a recent trend that aims at the provision of digital twins that can be connected in a modular library in powerful workflows. Such workflows need to combine different computational methods for different scales such as DFT calculations on the quantum level, MD simulations on the atomistic level and FEM on the continuum scale. For effective scale-bridging simulations and parameter studies in multidimensional spaces, the simulations on each level still require substantial speed-ups, sometimes even by orders of magnitude.

Software development

CASA Team

support

Community support

Publications

In this page are listed all recent and past publications of the SDLQM team.

News and Events

Organization and participation to events, job and thesis offers, general news

Publications

In this page are listed all recent and past publications of the SDLQM team.

publications.png

CASA SDL Quantum Materials

The CASA SDLQM builds over an established collaboration between the SDLQM at JSC and the team of prof. Matteo Rizzi at PGI-8 over the development of HPC kernels for numerical computations based on Tensor Networks (TN) methods. Due to their growing relevance and range of applications, TN are not only used to study many-body quantum systems in Condensed Matter theory, but can also provide insights in the simulation of quantum circuits, AI networks, Optimization theory and Lattice Gauge theory.

gauges_scheme.png

HPC-ready functional renormalisation group methods for correlated electrons

Functional renormalisation group methods have become one of several standard methods to investigate models for correlated electron systems. In certain cases and under certain conditions the numerical implementation can highly profit from parallel HPC architectures. We develop methodical extensions and implement them in an evolving code base. Further more, we support a community of scientists in this field by providing support and assistance concerning the usage of HPC-systems, and organise regular workshops on this subject together with our partners.

Team

logo_light.jpg

quantumM.jpg

ChASE_Logo_RGB.jpg

HPC Tensors

Parallel and HPC numerical libraries

In Materials Science, numerical libraries play a fundamental role. We aim at designing numerical algorithms that can exploit knowledge extracted from Materials Science applications to increase parallel efficiency and minimize computational time. We develop the resulting algorithms and implement them into modern numerical linear algebra libraries tailored to heterogeneous computing architectures and modular supercomputers.

Green's functions methods in Materials Science

The objective is to improve the flexibility and accuracy of Green's functions methods 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 2D hetero-interfaces which can then be compared against the outcome of experimental tests.

Scientific Machine Learning in Quantum Materials

Simulation science and data science are becoming increasingly intertwined and mutually important. In the field of quantum simulations, Machine and Deep Learning techniques are becoming increasingly common. Despite this convergence between simulations and data, many aspects of learning from data are still unknown. We focus in the area of explainable Machine and Deep Learning applied to specific topics in Materials Science and Numerical Linear Algebra with the aim of further increase the scientific knowledge and accelerate its discovery.

logo_color.png

EoCoE - Energy oriented Center of Excellence

At the crossroads of the energy and digital revolutions, EoCoE develops and applies cutting-edge computational methods in its mission to accelerate the transition to the production, storage and management of clean, decarbonized energy. EoCoE offers a network of experts in HPC and sustainable energies to design powerful numerical simulations with unprecedented accuracy and performance.

HANAMI - HPC ALLIANCE FOR APPLICATIONS AND SUPERCOMPUTING INNOVATION: THE EUROPE - JAPAN COLLABORATION

ELution's main goal is to implement a new multidisciplinary concept to enhance annotation of metabolites by (a) integrating existing metabolomics platforms for more accurate combined annotation, and (b) supplementing mass spectral databases with synthetic mass spectra from quantum chemistry-based simulation methods. With this highly improved annotation of metabolites, we create new possibilities in data analysis and knowledge discovery.

Joint Lab Virtual Materials Design

Virtual materials development is a recent trend that aims at the provision of digital twins that can be connected in a modular library in powerful workflows. Such workflows need to combine different computational methods for different scales such as DFT calculations on the quantum level, MD simulations on the atomistic level and FEM on the continuum scale. For effective scale-bridging simulations and parameter studies in multidimensional spaces, the simulations on each level still require substantial speed-ups, sometimes even by orders of magnitude.

Software development

CASA Team

support

Community support

Publications

In this page are listed all recent and past publications of the SDLQM team.

News and Events

Organization and participation to events, job and thesis offers, general news

Publications

In this page are listed all recent and past publications of the SDLQM team.

publications.png

CASA SDL Quantum Materials

The CASA SDLQM builds over an established collaboration between the SDLQM at JSC and the team of prof. Matteo Rizzi at PGI-8 over the development of HPC kernels for numerical computations based on Tensor Networks (TN) methods. Due to their growing relevance and range of applications, TN are not only used to study many-body quantum systems in Condensed Matter theory, but can also provide insights in the simulation of quantum circuits, AI networks, Optimization theory and Lattice Gauge theory.

gauges_scheme.png