Highlights

  • A tool to explore the energy landscape of materials

    A new computational method allows to perform a thorough exploration of the energy landscape of magnetic materials, which often have many possible solutions to the electronic structure problem. It allows to access many different starting points in terms of which orbitals are occupied by the electrons, and uses a global  algorithm to search systematically for all possible local minima of the energy in a material. The method, as well as its validation on many magnetic systems, has just been published in npj computational materials by scientists from Nicola Marzari's lab at EPFL. 

  • A direct probe of the quantum geometry of materials

    MARVEL scientists at the University of Fribourg have devised new mathematical techniques and applied them to an experimental method called angle-resolved photoemission spectroscopy (ARPES) to measure the Berry curvature, a particular way by which the laws of quantum mechanics interact with the electronic structure of a material and dictate the possible behavior of its electrons. So far this fundamental quantum geometrical property can only be measured indirectly. The study, published in Science Advances, could pave the way to a better understanding of topological materials. 

  • International collaboration lays the foundation for future AI for materials

    Artificial intelligence (AI) is accelerating the development of new materials. A prerequisite for AI in materials research is large-scale use and exchange of data on materials, which is facilitated by a broad international standard. A major international collaboration including MARVEL and CECAM now presents an extended version of the OPTIMADE standard.

  • A chain of copper and carbon atoms may be the thinnest metallic wire

    Researchers at EPFL have employed computational tools to look for new 1-D materials that could be exfoliated from known three-dimensional crystals. Out of an initial list of over 780,000 crystals, they came up with a list of 800 1-D materials, out of which they selected the 14 best candidates - compounds that have not been synthesized as actual wires yet, but that simulations suggest as feasible. Among them is the metallic wire CuC2, a straight-line chain composed by two carbon atoms and one copper atom, the thinnest metallic nanowire stable at 0 K found so far. The article is published in ACS Nano.

  • In search of new alloys for aerospace applications

    A study by MARVEL researchers in Raju Natarajan's laboratory at EPFL has used computational methods to accurately describe the properties of a 6-component alloy made of aluminum, niobium, titanium, vanadium, zirconium and tantalum. This alloy has promising properties that could be applied to aircraft engines or nuclear applications, due to its microstructure comprised of a disordered solid solution matrix and embedded precipitates of an ordered phase. Predictions from ab-initio calculations are in excellent agreement with experiments, and the study also allowed to derive some design rules for experimentalists on how to improve the performance of the alloy.

  • AiiDA used to drive experiments for the first time, matched with Empa’s Aurora robotic platform

    A new study shows how MARVEL's computational workflow engine AiiDA can be used not only to run computer simulations, but also actual experiments. Researchers from PSI, Empa, EPFL, ETH Zurich, and Technische Universität Berlin interfaced AiiDA with a robotic platform that automates battery experiments. AiiDA takes case of controlling the experimental devices, archiving and analyzing the resulting data, a first step toward fully autonomous self-driving labs. 

  • Materials follow the 'Rule of Four', but scientists don’t know why yet

    A new study by MARVEL researchers describes an unexpected "rule" followed by about 60 per cent of structures included in large databases of computational and experimental materials: their primitive unit cells are made out of multiples of four atoms. The scientists tried many different explanations, considering the role of specific chemical elements as well as formation energy and symmetry, but a convincing explanation is yet to be found. Still, the scientists could use an algorithm to predict with high accuracy whether a given compound will follow the Rule of Four or not.

  • Computational study points to a promising Weyl semimetal

    EPFL scientists have studied in detail the electronic structure and magnetic properties of InMnTi2, a compound they had first identified as a candidate Weyl semimetal during a high-throughput study. Weyl semimetals have unusual electrical conductivity that make them interesting for several applications in quantum technologies, lasers, or advanced optics. Interestingly, this material was initially described as non-magnetic, but DFT + U calculations showed it is actually antiferromagnetic, a property that could be exploited for memory devices, sensors, or quantum computing.

  • Using machine learning to study the microscopic behavior of a solid-state electrolyte

    Scientists in Michele Ceriotti's lab at EPFL have used machine learning to paint a more precise picture of how charge transport happens in lithium thiophosphate,  a promising material for solid-state batteries. The group is now perfecting the study of this material by adding an analysis of thermal transport, which will be the topic of a new publication.

  • GPT-3 transforms chemical research

    A study by MARVEL researchers in Berend Smit's laboratory at EPFL shows that large language models such as GPT-3 can be used to simplify the application of artificial intelligence to chemical analysis, improving accuracy while drastically reducing the amount of data needed for training.  The model, trained with relatively few Q&As, correctly answered over 95% of very diverse chemical problems. The method is as easy as conducting a literature search, and is applicable to various chemical problems.

  • In search of muons: why they switch sites in antiferromagnetic oxides

    A study involving MARVEL scientists and just published in Physical Review Letters has found that in manganese oxide, a textbook antiferromagnetic material,  the site of an implanted spin-polarized muon is not well identified, but can change due to a previously neglected effect: magnetostriction.

  • An in-depth look at a high-temperature superconducting nickelate

    A new study by Philipp Werner's group at the University of Fribourg makes advancements in the theoretical description of the correlated electron state of La3Ni2O7,  which has recently been described as a superconductor with a critical temperature around 80 K. The use of  GW+EDMFT, a method developed within MARVEL over the last ten years, was crucial for the study.