The Fermi surface (FS) of a solid separates occupied from unoccupied electron states at zero temperature. This concept, essential for understanding the behavior of electrons in solids, is also a very useful tool for property predictions of metals and doped semiconductors, particularly when combined with DFT simulations. However, to achieve a good accuracy of the calculated properties, a very dense sampling of the FS in reciprocal space is required, and direct first-principles calculations are limited by the computational time. To circumvent this difficulty, interpolation schemes have been developed, but either their practical utilization is far from trivial, or their accuracy is limited. Interpolation using Maximally Localised Wannier Functions (MLWFs) allows obtaining accurate results and approaches are being developed in order to automate the process.
One of the objectives of the project "Fermi Surfaces from High-throughput Ab Initio Calculations for the Discovery of Exceptional Topologies" (FiSH4DiET) is to develop very general tools to obtain efficiently accurate Fermi surfaces from high-throughput ab initio calculations, and to compute the related properties automatically.
With these tools, a database of FSs will be generated and made available openly. The latter will allow for the discovery of materials with exceptional topologies (and hence properties) of the FS, such as quasi-2D materials, tiny-pocket materials in the ultra-quantum regime, multi-valley systems and Kohn anomalies. In addition, in FISH4DIET we develop automated workflows to generate, test and verify accurate pseudopotentials, and deliver them in open libraries. Pseudopotentials are an essential ingredient of most density-functional-theory (DFT) simulations, especially when adopting a plane-wave basis set: having a curated, high-quality set of pseudopotentials is essential to have precise DFT results.
The project is a collaborative effort of the MSD group with Université Catholique de Louvain in Belgium (Prof. Gian-Marco Rignanese)
Contact: MSD group
MARVEL Pillar 3: Digital Infrastructure of Open Simulations and Data
This is one of the four core pillars of the third phase of the NCCR MARVEL. The goal of this Pillar is to provide the critical digital infrastructure needed to enable materials design and discovery in the 21st century.
It will focus on 3 major objectives:
- prepare for the exascale challenge, both in terms of code performance and scalability on (pre-)exascale machines, and high-throughput performance to drive millions of individual HPC simulations;
- make all codes, algorithms and workflows widely usable in a simple and optimized way also by non-experts, via the services provided by the AiiDAlab infrastructure and the Quantum Mobile virtual machine; this will also ensure reproducibility of all research and facilitate FAIR access and sharing of the resulting data via the Materials Cloud portal;
- critically, deliver a self-sustaining long-term infrastructure for simulations for the whole Swiss materials-science research ecosystem (and beyond) also after the end of the NCCR, to deliver automated simulations as microservices, as a key component for autonomous materials discovery and characterization.
The project leaders of this pillar are Dr. Giovanni Pizzi (MSD group leader) and Dr. Joost VandeVondele (CSCS and ETHZ), and the principal investigators include Dr. Sara Bonella (CECAM and EPFL).
Contact: MSD group
MARVEL Pillar 4: Long-term Integration in the Swiss Scientific Landscape
This is one of the four core pillars of the third phase of the NCCR MARVEL. The goal of this Pillar is to plan, deploy, and ultimately phase-in the post-2026 embedding of MARVEL in the Swiss scientific landscape, in the form of core partnerships with PSI and Empa.
To reach these goals and objectives, two main efforts have to be coordinated: (i) development and implementation of reliable and robust open-source computational tools to understand, predict, and characterize experimental results, and (ii) their deployment as user friendly turn-key solutions. Resources are being invested in the creation of such tools, that boost cooperation between experiments and simulation. This enables high levels of standardization in how calculations for a specific experiment are performed and how results are analyzed and processed. Tools such as AiiDAlab are, in fact, specifically designed to enable experimentalists to independently access the submission of calculations and the analysis of the automatically processed data.
The project leaders of this pillar are Prof. Nicola Marzari (LMS laboratory head), Dr. Carlo Pignedoli (deputy group leader at Empa), Prof. Christian Rüegg (PSI director and SCD division head)
Contact: LMS laboratory
PREMISE (Open and Reproducible Materials Science Research) aims to establish, promote and facilitate the adoption of FAIR ORD practices in Materials Science, focusing on enabling the treatment, at the same level, of experimental and simulation data.
In PREMISE we develop metadata standards for machine-actionable interoperability between electronic lab notebooks (ELNs)/lab information management systems (LIMSs) and workflow management systems (WFMSs), and apply these standards to Materials Science ontologies. We then collect, design and disseminate best practices for generating ORD as a natural part of the research process, rather than as an additional duty for researchers. Our deliverables are demonstrated via concrete pilot use cases, chosen to be applicable to the broad field of Materials Science, and generalisable to other disciplines. We leverage two robust open platforms, developed and maintained within the ETH domain, compliant by design with FAIR requirements for experiments (openBIS) and simulations (AiiDA+AiiDAlab). We will bring them "to the next level" by implementing our novel set of ORD practices and demonstrating how an ELN/LIMS and a WFMS can be made seamlessly interoperable. We expect our deliverables to be essential components of the emerging field of autonomous laboratories, where automated simulations and robotic experiments are combined via artificial intelligence in closed feedback loops, ultimately accelerating materials discovery and characterisation.
The project is a collaborative effort of the MSD group with Empa (Nanotech@surfaces laboratory: Dr. Carlo Pignedoli, Materials for Energy Conversion: Prof. Corsin Battaglia) and ETHZ (Scientific IT Services: Dr. Bernd Rinn, Dr. Caterina Barillari, Dr. Henry Lütcke)
Contact: MSD group