Description
In today's data-driven world, High-Performance Computing (HPC) is an emerging reference platform that drives scien fic research and enables industrial innova ons - from energy and environment to healthcare,information and communications, manufacturing, safety and transporta on. HPC is now attracting the attention of an increasingly large number of companies that span a wide range of fields.
Context of the workshop
This workshop will bring into the spotlight the effectivve results of the MaX flagship codes (Quantum ESPRESSO, Yambo, Siesta, Fleur, CP2K, BigDFT and AiiDA) and the recent advances in computational materials research based on quantum physics and electronic structure methods that are enabled by frontier erevolution of HPC. MaX has radically enhanced the performance of the flagship codes in terms of scaling, robustness, and usability, and will make them ready for the forthcoming exascale hardware architectures.
MaX – ‘Materials design at the eXascale’ is a European Centre of Excellence for HPC working on quantum simulation codes. The renovation of the flagship codes is complemented by co-design activities ensuring thebest exploitation of pre-eXascale and eXascale architecture. The built ecosystem allows the convergence of massive HPC and HTC (high-throughput computing) efforts into HPDA (high-performance data analytics).
Scope of the workshop
- Learn about the MaX flagship codes on the present HPC platforms and its latest new capabilities and algorithms for the study of complex materials, properties and processes in realistic condition,far beyond the current realms.
- Present the latest activities and services that MaX offers to the wider industrial community in the field of HPC and materials design.
Who and why should attend
This workshop aims at gathering scientists and organisations active in the field of materials modelling, together with HPC and HTC experts in order to discuss the most recent advancements in the field, including, but not limited to:
- Advances in high-performance computing for materials science.
- New avenues from data analytics/artificial intelligence in materials science.
- High throughput computing for materials discovery.
- Trends in high-performance computing and codesign towards exascale.
- Novel algorithms for first-principles simulations.
