• “Amon”-based ML approach excels in modelling quantum properties of a wide range of systems

    Anatole von Lilienfeld, professor at the Institute of Physical Chemistry at the University of Basel and project leader of Incubator Project 2 at NCCR MARVEL, and colleague Bing Huang have developed  transferable quantum machine learning models that combine atom-in-molecule based fragments, dubbed “amons," with active learning to overcome challenges currently preventing the widespread application of first-principles-based exploration of chemical space. In the paper "Quantum machine learning using atom-in-molecule-based fragments selected on the fly," they demonstrate the efficiency, accuracy, scalability, and transferability of the models for important molecular quantum properties, such as energies, forces, atomic charges NMR shifts, polarizabilities, and in systems ranging from organic molecules to 2D materials and water clusters to Watson-Crick DNA base-pairs. The article was recently published in Nature Chemistry.

  • Systematic approach quantifies chemical diversity of different MOF libraries

    Researchers at EPFL including MARVEL Deputy Director Berend Smit and colleagues at MIT have introduced a systematic approach to quantifying the chemical diversity of different metal-organic framework material libraries and then using these insights to remove certain biases. Though their works is focused on MOFs because there has been exponential growth in the number of studied materials, the question of how to correctly sample material design space is relevant to many classes of materials.

  • Atomistic modelling probes the behavior of matter at the center of Jupiter

    Researcher Michele Ceriotti, professor at the department of Materials Science at EPFL and project leader of MARVEL’s Design & Discovery Project 1, and colleagues in Cambridge and Zürich have developed a physics-based machine learning approach to examine the behavior of hydrogen at extremely high pressures. The model reveals evidence of continuous metallization, and so has significant implications for planetary science. More fundamentally, it shows the way ahead for a simulation-driven change of the way we understand the behavior of matter in fields as diverse as drug development and alloys for automobiles. The paper has just been published in Nature. 

  • Materials Cloud, AiiDA, cornerstones of MARVEL open science strategy, feature in Scientific Data

    The latest issue of Nature group’s Scientific Data journal features papers on the Materials Cloud, an Open Science Platform designed to enable the seamless sharing of resources in computational materials science as well as AiiDA, an open-source Python infrastructure that helps researchers automate and share computational workflows. Publication is a testimony to the ever-increasing adoption of the two tools that emerged from EPFL Professor Nicola Marzari’s Theory of Simulation and Materials (THEOS) group, and now the cornerstones of NCCR MARVEL’s Open Science strategy. 

  • Non-Abelian topological charge explains why certain Weyl points with different chirality do not annihilate upon collision

    Common wisdom holds that two Weyl points with different chirality will annihilate when they collide with each other. Now, combining theoretical arguments with first-principles calculations, researchers including Oleg Yazyev, Chair of Computational Condensed Matter physics at EPFL and scientist  QuanSheng Wu have predicted, on the contrary, that this is not the case in Weyl points (WP) occurring near the Fermi level of zirconium telluride. In the paper Non-Abelian reciprocal braiding of Weyl points and its manifestation in ZrTe, recently published in Nature Physics, they report their finding that a pair of Weyl points in the C2T invariant plane around the K point carry non-trivial values of the non-Abelian charge, the Euler number, and will not annihilate, but rather convert into a nodal line (NL) in the mirror invariant plane when applying uni-axial strain along the [001] direction.

  • MARVEL research highlighted in JCP special issue on machine learning in chemical physics

    Edited by NCCR MARVEL group leaders Michele Ceriotti and Anatole von Lilienfeld as well as colleague Cecilia Clementi of Rice University, the JCP Special Topic on “Machine Learning Meets Chemical Physics” features papers from the labs of Clemence Corminboeuf and Ceriotti at EPFL and von Lilienfeld at the University of Basel. The issue focuses on research that puts an emphasis on the interplay between machine learning and chemical physics, whether by incorporating physical principles and chemical intuition into model construction or by using machine learning to recognize new laws or general design principles. In the article below, we look at highlights of the research contributed from the three MARVEL labs. 

  • 2-D materials for ultrascaled field-effect transistors: One hundred candidates under the ab initio microscope

    Researchers led by Mathieu Luisier from the Institute for Integrated Systems (IIS) at ETH Zurich and NCCR MARVEL's Director Nicola Marzari at EPFL have set out to develop a comprehensive atlas of 2-D materials that might be capable of challenging currently  manufactured silicon-based transistors, so-called Si FinFETs. The new simulations are based on earlier results from Marzari and his team, who used complex simulations on the Swiss National Supercomuting Centre's supercomputer "Piz Daint" to sift through a pool of more than 100,000 materials to identify 1,825 from which it might be possible to obtain 2-D layers of material. The paper 2‑D Materials for Ultrascaled Field-Effect Transistors: One Hundred Candidates under the Ab Initio Microscope, recently published in the journal ACS Nano, identifies 13 particularly promising candidates. 

  • Automated Wannierisation for high-throughput computational materials design

    High-throughput computational materials design is an emerging field that looks set to accelerate reliable, cost-effective design and optimisation of new materials that feature specific desirable properties. Maximally-localized Wannier functions (MLWFs)—a means of representing the Bloch eigenstates of a periodic system—are tools that have been regularly used to compute certain advanced materials properties from first principles. Bringing the two approaches together has been complicated by the fact that generating MLWFs automatically and robustly without user intervention and for arbitrary materials is difficult. Researchers at Nicola Marzari’s THEOS lab and colleagues have now addressed this problem by proposing a procedure for automatically generating MLWFs for use in high throughput frameworks. In the interest of Open Science, they have also developed a virtual machine that allows researchers to perform their own simulations, either with different parameters or on new materials using this new protocol. The research was a collaboration between the EU H2020 E-CAM and MaX Centres of Excellence. A Success Story on the project can be found here.

  • AiiDA lab allows researchers to “focus on science rather than on setting up simulations”

    Dr. Carlo Pignedoli, deputy group leader of the Atomistic Simulations Group at the nanotech@surfaces Laboratory of Empa, has been given special recognition by his laboratory head for two years in row. His feat? Following a record number of projects from experimental colleagues and coordinating simulations in the field of carbon-based nanomaterials, one of the lab’s core activities, also associated with MARVEL Design & Discovery Project 3 – Low Dimensional Materials. For Pignedoli, the achievement was made possible by excellent researchers, certainly, but also by the exceptional efficiency in performing computational tasks thanks to AiiDA, open-source infrastructure for managing and storing the ever-growing amount and complexity of workflows and data in computational science. “High computational efficiency is what made the difference in the last two years compared to the past and it's all about AiiDA and AiiDA lab,” Pignedoli said. “With this "success story" I would like to acknowledge the AiiDA & AiiDA lab teams for all their work and for continuously inspiring new solutions for boosting computational materials science.”

  • Strength of high-entropy alloy CoCrFeNiPd linked to large Pd misfit volume, theory shows

    Recent experiments have shown that adding palladium (Pd) to the high-entropy alloy CoCrFeNi produces a material that is significantly stronger. In the paper Origin of high strength in the CoCrFeNiPd high-entropy alloy, NCCR MARVEL researchers led by Prof. Bill Curtin, head of EPFL’s Laboratory for Multiscale Materials Modelling, show how a recent parameter-free theory for initial yield strength in random alloys predicts a strength of CoCrFeNiPd that is in good agreement with experimental results. The strengthening is mainly due to the large misfit volume of Pd in CoCrFeNi.

  • Dialing flat bands in twisted double bilayer graphene

    Researchers including Professor Oleg Yazyev, chair of Chair of Computational Condensed Matter Physics at EPFL, and scientist QuanSheng Wu have shown that flat bands are a fundamental feature twisted double bilayer graphene (TDBG) rather than the result of any external fields and have identified a so-called magic angle of 1.3 degrees, at which both electron and hole gaps are maximized. A separate paper, published together with experimental colleagues in China, shows that TDBG is an easily tunable platform for exploring quantum many-body states thanks to vertical displacement fields. The corresponding papers were recently published in Nano Letters and Nature Physics, respectively. 

  • New experimental and theoretical evidence identifies jacutingaite as a dual-topology insulator

    New collaborative work involving NCCR MARVEL researchers has provided additional insight into the nature of jacutingaite (Pt2HgSe3), a species of platinum-group mineral first discovered in a Brazilian mine in 2008. The new studies show that the material is one of only a few known dual-topological insulators, featuring different surface states that are linked to crystalline symmetries rather than to the topological properties of the 2D monolayer, which is a quantum spin Hall insulator (QSHI). The work has been published in Physical Review Letters and Physical Review Research.