• Enhancing disorder to create order

    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.

  • New Machine Learning Approach Speeds Investigation of Chemical Shifts in Molecular Solids

    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.

  • New Convex Hull Framework Provides More Efficient Means of Identifying Synthesizable Materials Candidates

    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.  

  • The Science Behind Modeling Materials at the Atomic Scale

    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.

  • Machine learning and volcano plots: a very 21st century search for the philosopher’s stone

    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.  

  • Magnetic spirals for spiralling data

    With internet use ever expanding, there is an urgent need for more energy efficient data storage. Current technology records data in the magnetic state of a material using a magnetic field generated by an electric current. Energy efficiency would be enormously improved if we could replace the magnetic field by an electric field. Manipulating a material’s magnetisation by an electric field is not straightforward, however. Multiferroic magnetic spirals are one of very few ways this might be achieved, but these tend to be stable only at temperatures way below normal device operating conditions. In a series of papers, MARVEL groups led by Nicola Spaldin at ETHZ and Marisa Medarde at PSI show that magnetic spirals can be tuned and stabilised beyond room temperature by manipulating chemical disorder. This new mechanism may be exploited in the design of energy efficient data storage devices where the magnetisation is controlled by an electric field.

  • MARVEL labs collaborate to revolutionize computational metallurgy

    Close collaboration between two NCCR MARVEL labs may soon result in a fundamental change to the traditional simulation approaches in computational metallurgy and a deeper understanding of how processing and composition affect the properties of metals and their alloys.

  • Web platform “Materials Cloud” could help industry streamline research efforts

    Materials Cloud (www.materialscloud.org), a new web platform developed to help computational materials scientists share their work and promote open science, may also offer advantages to industrial partners more concerned with IP.

  • First principles physics from a Brazilian mine

    Quantum spin Hall insulators are part of a new class of materials, the so-called topological materials. Topological materials host exotic phases of matter that are protected by topological properties of the quantum state. They are of great interest for reasons both fundamental and applied. Notably, they would open a window into the quantum world through condensed matter physics. For all their theoretical promise however, there is a scarcity of experimentally known materials that exhibit a robust topologically non-trivial phase. In their paper Prediction of a Large-Gap and Switchable Kane-Mele Quantum Spin Hall Insulator, Antimo Marrazzo, Marco Gibertini, Davide Campi, Nicolas Mounet and Nicola Marzari identify one such material, showing that a monolayer of the mineral Jacutingaite realises the Kane-Mele model for a quantum spin Hall Insulator.

  • What MARVEL is doing is a dream come true for researchers in the field

    Scott Auerbach, Professor of Chemistry and Chemical Engineering at the University of Massachusetts, Amherst, USA, arrived at MARVEL as a visiting professor at the beginning of February. Here for five months, he is continuing his research on zeolites, using simulations to better understand the chemistry that goes on inside these nanoporous materials as well as investigating how they form in the first place. He is also skiing and enjoying the lake. Here is a conversation with Prof. Auerbach to get his take on MARVEL.

  • ChemAlive - MARVEL collaboration wins grant to boost machine learning approaches to real-time chemical and reaction modelling

    Founded in December 2014, ChemAlive (www.chemalive.com) has already gained contract business with customers such as Biosynth in Switzerland and internationally with Saudi Aramco (Aramco Services Center) to validate quantum chemistry in an industrial setting and guide the design of specific on-line tools to be launched as a software as a service (SaaS) in the third quarter of 2018. ChemAlive has also won the support of influential start-up accelerators, taking away the Gold Award, for example, from the MassChallenge Accelerator.

  • 2D or not 2D? MARVEL algorithm answers the question

    Two-dimensional materials, such as graphene, are a new and exciting class of materials. No more than a few atomic layers thick, they have the most extraordinary properties, making them attractive for all kinds of applications. However, despite high expectations, progress in identifying new 2D materials has been slow: to date, only a few dozen have been identified experimentally. In their just-out Nature Nanotechnology paper, which made the cover page, "Two-dimensional materials from high-throughput computational exfoliation of experimentally know compounds", a team led by MARVEL director Nicola Marzari takes a computational route towards improving that count.