Simulation and Data Laboratory Quantum Materials

SDL Quantum Materials

The Simulation and Data Laboratory Quantum Materials (SDLQM) provides expertise in the field of quantum-based simulations in Materials Science with a special focus on High-Performance Computing and scientific Machine Learning. SDLQM acts as a high-level support structure in dedicated projects and hosts research projects dealing with fundamental aspects of method development, algorithmic optimization, and performance improvement. The Lab acts as an enabler of large-scale simulations on current HPC platforms as well as training learning models based on in-silico simulations.

Dr. Edoardo Di Napoli

Head of SDL Quantum Materials

  • Institute for Advanced Simulation (IAS)
  • Jülich Supercomputing Centre (JSC)
Building 14.14 /
Room 4017
+49 2461/61-2527


  • The development of a numerical library (JuNLib) tailored to computational tasks emerging from Materials Science codes;
  • The optimization and modernization of Materials Science codes running on supercomputing architectures;
  • The maturation of mathematical models and tools for the advancement of simulation methods in Materials Science;
  • The execution of large scale simulations and their analysis;
  • Supporting flexible and sustainable programming practices for software developed in the Materials Science community;
  • The maintenance of a team of qualified scientists and technicians through funding initiatives;
  • The organization of workshops, symposia and courses pertaining to HPC and simulations in the realm of Quantum Materials Science.


The SDLQM 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.

Research Areas

Projects and collaborations

Software development

Team, support, publications and news

Last Modified: 19.09.2023