• 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.

  • 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.

  • 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.

  • 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. 

  • AiiDA manages, preserves and disseminates the simulations, data and workflows of modern computational science

    Advances in high-performance computing, improvements in the computer codes behind quantum–mechanical simulations and the emergence of curated materials databases have turned high-throughput computing into an essential tool in materials discovery and design.  

  • MARVEL team shows how properties of amorphous aluminum oxide can be tuned by electrochemical anodizing

    Research groups supported by NCCR MARVEL used a combination of experimental and theoretical methods to show that structural and dielectric properties of amorphous aluminum oxide can be tuned through electrochemical anodizing, an industrially relevant surface treatment technique.

  • Machine learning accelerates discovery of new 2D structure of titanium dioxide

    A new combination of structure prediction and machine learning methods has led to the discovery of a new 2D titanium dioxide structure that could eventually be used in hydrogen generation and energy storage.

  • New machine learning approach could accelerate materials optimization and drug discovery

    Researchers have developed a machine-learning model that may greatly accelerate drug discovery by accurately predicting the interactions between a protein and a drug molecule using only a handful of reference experiments or simulations. The algorithm, which can also tackle materials science problems such as modelling the structure of silicon surfaces, promises to revolutionise materials and chemical modelling, and gives insight into the nature of intermolecular forces.

  • Machine learning presents a huge opportunity to identify new materials at a reduced cost

    A new machine learning-based approach to predicting the properties of materials cut the computing power needed to analyze two million crystals from more than 20 million hours on a supercomputer to an afternoon on a laptop, the cost from as much as CHF 2 million to, essentially, nothing, and identified 90 previously unknown, thermodynamically stable crystals in the process.