Highlights

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.

EPFL Professor and NCCR MARVEL researcher Michele Ceriotti and colleagues at the University of Cambridge in the U.K. have developed a computational method to more efficiently identify materials candidates that are likely synthesizable.  

There’s a lot of mystique around quantum mechanics, but it’s actually very simple, says Nicola Marzari, director of the Swiss National Centre of Competence in Research (NCCR) MARVEL, a center for the computational design and discovery of novel materials.

Catalysts are essential to an endless number of chemical reactions. The right catalyst can make the difference between a process that is industrially viable and one that is not. Identifying new and better catalysts is therefore an important focus of chemical research. In a series of papers, Prof. Clemence Corminboeuf (EPFL) and her colleagues have explored the possibility of extending the use of volcano plots to identify ideal catalysts from heterogeneous and electro-catalysis to homogeneous catalysis. Teaming up with the group of Prof. Anatole von Lilienfeld (University of Basel), the latest paper in the series moreover shows how using quantum machine learning models in combination with volcano plots could considerably speed up discovery.