Common workflows for computing material properties with various quantum engines

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.   

OPTIMADE API enables seamless access and interoperability across materials databases

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. 

Newly identified R-2 2D material may show promise in development of spin-layer-locking spinFETs

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.

First-ever rare earth nickelate single crystals lead to first experimental evidence supporting predicted multiferroicity

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.

Machine learning cracks the oxidation states of crystal structures

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.

A new collaboration points to the future of data

In collaboration with MARVEL and EPFL, the Paul Scherrer Institute (PSI) is officially expanding its own focus areas and establishing a new research division: Scientific Computing, Theory, and Data. Here researchers will increasingly focus on the development of new computer and data technology and its use in science. The new research division is PSI's sixth, joining five previously established divisions: Biology and Chemistry; Research with Neutrons and Muons; Nuclear Energy and Safety; Energy and Environment; and Photon Science.

MARVEL Distinguished Lecture — John Perdew

The 27th NCCR MARVEL Distinguished Lecture will be given by Prof. John Perdew, Professor of physics and chemistry at Temple University on "More-predictive density functionals, symmetry breaking, and strong correlation"