Highlights

Understanding how defects can affect ground-state properties, promote phase transitions, or enable entirely new functionalities in some strongly correlated oxides has become a subject of major interest in the field of design and discovery of novel functional materials. SrMnO3 (SMO) is a particularly interesting example, but better characterization is needed. MARVEL researchers have now a developed a method that may lead to more accurate predictions of the energetics of defects associated with in-gap states in semiconductors or insulators.

EPFL chemists have developed a new iron-nickel oxide catalyst for water splitting, the reaction that produces hydrogen fuel. The patent-pending catalyst shows significantly higher activity in the oxygen-evolution part of reaction than conventional nickel iron oxide catalysts. The work, now published in ACS Central Science, was supported by the density functional theory (DFT) computations of NCCR MARVEL's Clémence Corminboeuf and her postdoctoral student Michael Busch: their work underpinned the possible theoretical explanations.

Transforming emitted CO2 into valuable products has been proposed as a way of reducing the amount of this greenhouse gas released into the atmosphere—using it as a raw material could help both close the carbon cycle and reduce the consumption of petrochemicals. Dr. Kyriakos C. Stylianou of EPFL and NCCR MARVEL and EPFL's Professor Paul Dyson have developed a recyclable catalyst that can be used to produce valuable products. The research has been published in Angewandte Chemie.

NCCR MARVEL’s Michele Ceriotti and Clemence Corminboeuf have joined forces to develop an innovative machine learning model for the electron density. Knowledge of a system’s electron density gives access in principle to all its ground state properties. However, the computations needed to determine the electronic structure from first principles remain costly. A machine learning approach promises to lighten this computational burden significantly.

Topological materials – unusual materials whose surface properties are different from those in the bulk – have generated significant interest in recent years because of their unique characteristics. Topological insulators, for instance, are electrical insulators in the bulk, but conduct electricity on their surfaces or edges.

Researcher Kyriakos Stylianou from the lab of NCCR MARVEL's deputy director Berend Smit and colleagues have developed a portable and user-friendly device that can measure fluoride concentration accurately and reliably.

Researchers from the lab of NCCR MARVEL's deputy director Berend Smit and colleagues have developed a methodology for collecting the lessons learned from partially failed trials and incorrect hypotheses -- the experiments that didn't work. The research was published in Nature Communications.

Research from NCCR MARVEL scientists and colleagues resulted in not one but two Editors' Suggestions in Physical Review Materials in November.  

MARVEL researchers were part of a group that used ultrafast X-ray diffraction to show how photodoping triggers a purely structural phase transition in a perovskite. The research was published in Physical Review Letters.

Researchers from the lab of NCCR MARVEL's deputy director Berend Smit and colleagues have developed a photocatalytic system that is based on a material in the class of metal-organic frameworks. The system can be used to degrade pollutants present in water while simultaneously producing hydrogen that can be captured and used further. The research was published in Advanced Functional Materials.

Considering how it can unexpectedly screw up almost anything, from Napoleon’s military campaign to your medical treatment, it would be nice to be able to control polymorphism: to have a way to predict whether a substance has polymorphs, and if so, which polymorphs form under which conditions. In a recent paper, MARVEL researchers Pablo Piaggi and Michele Parrinello set out to understand the phenomenon.

EPFL scientists including NCCR MARVEL's Michele Ceriotti have developed a machine learning method to predict chemical shifts of molecular solids with an accuracy comparable to that derived from electronic-structure calculations—but at a much faster speed and lower computational cost. The research was published in Nature Communications.