Highlights

NCCR MARVEL researchers at the University of Fribourg have tested the GW + EDMFT ab initio approach for correlated materials modelling. Using the insulator-metal transition in the perovskite Ca2RuO4 as a benchmark, they found that their parameter-free simulation was in close agreement with the available experimental data and that the extension to the nonlocal polarization and self-energy provided by GW are essential to attaining such accuracy. The calculations represent an important test and an encouraging result for the further application and development of the GW + EDMFT framework, the authors said.

A team of EPFL researchers has combined a large database of 3D structures with a machine learning model of chemical shifts and topological representations of molecular environments to allow for the probabilistic assignment of NMR spectra of organic crystals directly from their 2D chemical structures. They demonstrated the approach on seven molecular solids with experimental shifts and benchmarked it on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in more than 80% of cases. The paper, Bayesian Probabilistic Assignment of Chemical Shifts in Organic Solids, was published today in Science Advances.

Perovskites—materials with crystal structures similar to that of calcium titanium oxide—have unique properties. Rare earth nickelates such as RENiO3, for instance, are metallic at high temperatures, but insulating and magnetically ordered at low temperatures. Moreover, it has been theoretically predicted that these materials might be multiferroic, that is, featuring simultaneously occurring ferroelectric and magnetic order in the low temperature phase. While the materials have drawn much attention for potential applications in fields of research ranging from optoelectronics, to battery engineering and neuromorphic computation, crucial experimental data needed to validate theoretical predictions has been lacking because the materials are very difficult to synthesize—to date, it has not been possible to grow sizable bulk single crystals based on rare earths other than La, Pr and Nd. Now, MARVEL researchers at PSI and colleagues have successfully grown bulk single crystals of the full nickelate family while another team including former MARVEL members from the University of Geneva has used them to provide experimental evidence supporting the existence of multiferroicity in these materials.

Correlated materials, which feature highly localized electrons and strong coupling between electrons, their spin, and atomic vibrations, are among the most mysterious and exotic of all solids. They can host states of matter ranging from high-temperature superconductivity to metal-nonmetal transitions, colossal magnetoresistance and multiferroicity. Much-needed theoretical research into these materials is nonetheless hampered by a lack of quantitative methods capable of accurately describing the electron-phonon interactions that play a critical role in determining their unique properties. In a letter just published in Physical Review Letters, a team of researchers from EPFL, Caltech and colleagues introduce an ab initio approach that allows for the quantitative calculations of such interactions. The method can be broadly applied to various families of strongly correlated materials, capturing the strong coupling of electron, spin and lattice degrees of freedom and their combined effect on electron-phonon interactions, paving the way for quantitative studies of their rich physics.

Today’s electronic devices rely on the electron’s negative charge to manipulate electron motion or store information. So-called spintronic devices, which would also exploit the spin of electrons for information processing and storage, may ultimately allow us to reduce energy consumption while increasing information processing capabilities, giving us multi-functional, high-speed, low-energy electronic technologies. An essential first step, however, is finding appropriate high-performance materials and integrating them into devices that allow us to control their properties well. In the paper “Gate Control of Spin-Layer-Locking FETs and Application to Monolayer LuIO,” recently published in Nano Letters, NCCR MARVEL researchers and colleagues identified lutetium oxide iodide (LuIO) as just such a material. They studied how to control its properties with electric gates—simulating for the first time ever the effect of electronic doping—and provided practical guidelines for building and operating associated devices from such material.

Using electronic-structure simulations based on density-functional theory to predict material properties has become routine, thanks at least in part to an ever-widening choice of increasingly robust simulation packages. This wide selection of codes and methods allows for cross-verification, useful in ascertaining accuracy and reliability. But the wide range of methods, algorithms and paradigms available make it difficult for non-experts to select or efficiently use any one for a given task. In the paper “Common workflows for computing material properties using different quantum engines,” published today in npj Computational Materials, a team led by researchers in NCCR MARVEL and in the MaX CoE  shows how the development of common interfaces for workflows that automatically compute material properties can address these challenges and demonstrate the approach with an implementation involving 11 different simulation codes. Also thanks to the use of the AiiDA workflow engine, they guarantee reproducibility of the simulations, simplify interoperability and cross-verification, and open up the use of quantum engines to a wider range of researchers.   

More than 30 research institutions including NCCR MARVEL have come together to form the Open Databases Integration for Materials Design consortium and develop an API specification enabling seamless access and interoperability among materials databases. The paper “OPTIMADE, an API for exchanging materials data,” published today in Nature Research’s journal Scientific Data, presents the OPTIMADE API specification, illustrates its use and discusses future prospects and ongoing development. 

Chemical engineers at EPFL have developed a machine-learning model that can predict a compound’s oxidation state, a property that is so essential that many chemists argue it must be included in the periodic table.

All so-far experimentally determined Weyl semimetals (WSMs) have featured Weyl points—crossings of linearly dispersing energy bands of three-dimensional crystals—that appear in pairs in the momentum space. Now, NCCR MARVEL researchers have combined experimental and theoretical methods to show that the WSM platinum gallium PtGa features a singular, unpaired Weyl point surrounded by closed Weyl nodal walls (WNW). This is the first time that an unpaired electronic WP has been observed experimentally and the first time that electronic topological WNWs/surfaces have been experimentally reported in crystalline solids. The findings of teams led by Prof. Oleg Yazyev, Chair of Computational Condensed Matter Physics at EPFL and Prof. Ming Shi of the Spectroscopy of Novel Materials group at the Paul Scherrer Institute extend our understanding of basic topological physics and the application of Weyl semimetals into spintronics.

The modelling of some molecular systems requires an explicit description of changes in the electronic structure. While quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed for such cases, they are computationally expensive compared to fully classical simulations. In a paper recently published in the Journal of Chemical Theory and Computation, ETH Prof. Sereina Riniker, group leader in NCCR MARVEL Design and Discovery Project 1, and colleagues Lennard Böselt and Moritz Thürlemann show how using a machine learning (ML) approach that combines high-dimensional neural network potentials (HDNNPs) and Δ-learning reduces the cost of modelling while retaining accuracy in (QM)ML/MM MD simulations of condensed-phase systems.

Two recently published papers from a collaboration between two NCCR MARVEL labs have identified a new type of defect as the most common source of disorder in on-surface synthesized graphene nanoribbons (GNRs), a novel class of carbon-based materials that may prove extremely useful in next-generation electronic devices. Combining scanning probe microscopy with first-principles calculations allowed the researchers to identify the atomic structure of these so-called "bite" defects and to investigate their effect on quantum electronic transport in two different types of graphene nanoribbon. They also established guidelines for minimizing the detrimental impact of these defects on electronic transport and proposed defective zigzag-edged nanoribbons as suitable platforms for certain applications in spintronics.

Perovskite solar cells (PSCs) are among the most promising and cheapest photovoltaic technologies now available, but widespread application has been hampered by issues linked to long-term stability and processability. In the paper A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3, recently published in Science Advances, researchers including Prof. Michele Parrinello, professor of computational Sciences at the Università della Svizzera italiana and ETHZ as well as Group Leader in NCCR MARVEL's Design & Discovery Project 1, and Paramvir Ahlawat,  a PhD student in the EPFL Lab of Prof. Ursula Roethlisberger, address this problem with a combined experimental and simulation study that could improve the design of industrial-scale processing techniques for MAPbI3 and FAPbI3, two lead perovskites.