Highlights

  • AiiDA helps automating calculations for muon spectroscopy

    A collaboration between groups in Switzerland and Italy has proposed a fully automated workflow that makes it much easier for scientists to calculate the stopping site of muons and their interaction with the environment during muon spectroscopy, increasing the power of this experimental technique when it comes to studying fine details in the magnetic properties of materials.  The workflow integrates existing codes and libraries with newly developed algorithm and takes advantage of the AiiDA infrastructure. The article that describes it was published in Digital Discovery.

  • How to combine quantum and classical algorithms for materials simulation

    A study by MARVEL researchers presents a new framework to handle hybrid quantum-classical algorithms for materials simulations. The framework works by interfacing two widely used software tools that belong respectively to the classic and quantum world: CP2K and Quiskit Nature.  The key step is to identify the "active space" in a material - the orbitals and electrons that are key for the properties of interest. Quantum algorithms are used to simulate the active space at a higher level of detail, while classical ones are applied to the rest of the system.  When tested on magnesium oxide, the workflow produces results that are in very good agreement with those from state-of-the-art theoretical and experimental methods. The study is published in npj Computational Materials.

  • New machine learning approach enables accurate determination of Hubbard parameters at virtually no cost

    Scientists at EPFL and the Paul Scherrer Institute have shown that machine learning can reduce the time and computational cost of density-functional theory with extended Hubbard functionals, a widely used method that allows to simulate complex materials containing transition-metal or rare-earth elements. Using a recently developed class of neural networks called “equivariant neural networks”, and a dataset of 12 materials spanning various crystal structures and compositions, the team trained two separate models – one for the U parameter and one for the V – to work independently of one another. The models performed very well in calculating both the U and V parameters themselves, as well as some downstream properties such as magnetic moments or voltages. The study is published in npj Computational Materials.

  • Mapping the ecosystem of Wannier Functions software

    A new review article, just published in Reviews of Modern Physics and highlighted on the journal cover, provides a map to the vast landscape of software codes that allow researchers to calculate Wannier functions, and to use them for materials properties predictions.  The authors, from all over Europe and the USA, include three current MARVEL members and three former ones. After providing readers with the theoretical foundations on Wannier functions and their calculation, together with intuitive graphical schematics to explain what Wannier functions are, the authors map the existing Wannier codes and the key applications. Several codes that now make up the Wannier ecosystem were developed within or with the support of MARVEL.

  • How machine learning can help predict the spectral properties of materials

    MARVEL scientists at the Paul Scherrer Institute and the University of Zurich have used a machine learning model to calculate the screening parameters for Koopmans functionals, a promising approach to expand the power of density-functional theory so that it can be used to predict the spectral properties of materials. The study, published in npj Computational Materials, focussed on two model systems: liquid water and the halide perovskite CsSnI3. Even with a relatively simple network and learning model, the scientists were able to significantly reduce the computational cost of the algorithm, paving the way to a more efficient calculation of the temperature-dependent spectral properties of interesting materials. 

  • New widgets and extensions expand the OSSCAR platform for educational notebooks in materials science

    In a new article published in Computer Physics Communications, the team of the Open Software Services for Classrooms and Research project (OSSCAR) describes how to create custom widgets and extensions that can be used in interactive notebooks to teach computational materials science. The article also introduces two new entries in OSSCAR: a widget to display an interactive periodic table that allows users to group elements into different states, and one to plot and visualize electronic band structures and density of states.

  • In search of the perfect materials for fusion reactors

    Can theory and computation methods help the search for the best divertor material and thus contribute to making fusion a reality? Scientists in Nicola Marzari’s MARVEL laboratory at EPFL decided to answer the question, and in a new article they present a method for a large-scale screening of potential materials to be used in a nuclear fusion divertor, a component that has to withstand extreme heat and a bombardment of particles. The shortlist of the most promising materials contains tungsten, that has been chosen for the ITER reactor, together with other options that may be considered for future reactors.

  • Orbitronics: new material property advances energy-efficient tech

    Orbital angular momentum monopoles have been the subject of great theoretical interest as they offer major practical advantages for the emerging field of orbitronics, a potential energy-efficient alternative to traditional electronics. Now, through a combination of robust theory and experiments at the Swiss Light Source SLS at Paul Scherrer Institute PSI, their existence has been demonstrated. The discovery is published in the journal Nature Physics.

  • A new benchmark to recognize the hardest problems in materials science

    A large collaboration led by MARVEL's Giuseppe Carleo has  introduced a method to compare the performance of different algorithms, both classical and quantum ones, when simulating complex phenomena in condensed matter physics. The new benchmark, called V-score, is described in an article just published in Science and has been validated on several examples of quantum many-body problems, pointing to the ones where future quantum computing algorithms may really make a difference. 

  • The story of Jacutingaite: how a wonder material went from the mine to theory, crystal growth and experiments

    The first article of a series about MARVEL's success stories from its 10 years of research. In this story, we revisit how a close collaboration between theorists and experimentalists led to identify, synthesize and test a unique exotic material that until then had only appeared in some samples from a Brazilian mine. The material, called jacutingaite and with the composition Pt2HgSe3, was eventually confirmed to be the first ever material showing the so-called Kane-Mele physics, a quantum phenomenon that had been predicted but never seen in action before. Research is still ongoing on the original jacutingaite and on other materials of its family, and could lead to several technological applications. 

  • Computational marathon matches the efficiency of the AiiDA platform with the power of Switzerland Alps supercomputer

    A group of MARVEL researchers from the Paul Scherrer Institute has conducted a "hero run" on the new Swiss supercomputer, occupying it entirely for about 20 hours with calculations managed remotely by the AiiDA software tools. The run demonstrated the efficiency and stability of AiiDA, that could seamlessly fill the entire capacity of an exascale machine, as well as the performance of the Alps supercomputer, that has been just inaugurated. All the results will soon be published on the Materials Cloud.

  • The best of both worlds: combining accurate spectroscopy and thermodynamics for correlated materials

    A new mathematical framework developed by MARVEL scientists at EPFL allows to calculate the spectrum of a material and its thermodynamics behavior at the same time, including the total energy and the band structure, and it does this even for complex, correlated materials. The method, published in an article in Physical Review Research, combines thermodynamics from DFT+U and spectra from GW, and was validated on transition metal oxides.