Highlights

Two-dimensional superconductors have drawn considerable attention both for the fundamental physics they display as well as for potential applications in fields such as quantum computing. Although considerable efforts have been made to identify them, materials with high transition temperatures have been hard to find. Materials that feature both superconductivity and non-trivial band topology, a combination that could potentially give rise to exotic states of matter, have proven even more elusive. In the paper Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3 , recently published in Nano Letters, researchers predict just such a material in the easily exfoliable, topologically non-trivial 2D semimetal W2N3.

Combing through the “vast virtual set of all conceivable stable combinations of elements and structural configurations which form matter” is a daunting task in materials design. In the paper Simplifying inverse material design problems for fixed lattices with alchemical chirality, Anatole von Lilienfeld, professor at the Institute of Physical Chemistry at the University of Basel/University of Vienna and project leader of NCCR MARVEL Incubator Project 2 and postdoc Guido Falk von Rudorff, show how the concept of 4-dimensional chirality resulting from an alchemical reflection plane in the nuclear charge space allows researchers to break down combinatorial scaling. This alchemical chirality and the simplifications that it allows deepen our understanding of chemical compound space and enable researchers to establish new trends ‘on-the-fly,’ without resorting to empirical observation. The paper is forthcoming in Science Advances. 

Researchers led by Oleg Yazyev, head of the Chair of Computational Condensed Matter Physics at EPFL, have determined a straightforward method for probing the topological character of electronic bands in two-dimensional Moiré superlattices using Landau level sequences. The results can be easily extended to other twisted graphene multilayers and h-BN/graphene heterostructures, making the approach a powerful tool for detecting non-trivial valley band topology. The paper Landau Levels as a Probe for Band Topology in Graphene Moiré Superlattices was recently published in Physical Review Letters.

Pinning down the nature of bulk hydrated electrons—extra electrons solvated in liquid water—has proven difficult experimentally because of their short lifetime and high reactivity. Theoretical exploration has been limited by the high level of electronic structure theory needed to achieve predictive accuracy. Now, joint work from teams at the University of Zurich and EPFL and colleagues has resulted in a highly accurate machine-learning (ML) model that is inexpensive enough to allow for a full quantum statistical and dynamical description, giving an accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron. This new approach, outlined in the Nature Communications paper Simulating the Ghost: Quantum Dynamics of the Solvated Electron, could also be applied to excited states and quasiparticles such as polarons and would allow for high-accuracy simulations at a moderate price.

The combination of electronic structure calculations and machine learning (ML) techniques has become commonplace in atomistic modelling—ML interatomic potentials, for example, can now describe the potential energy surface of a material across many phases, including a wide range of defects. Looking ahead, however, it is the calculation of ML models that can predict properties beyond the interactions between atoms that might eventually allow integrated machine learning models to replace costly electronic structure calculations entirely. In the paper Learning the electronic density of states in condensed matter, recently published in Physical Review B, researchers led by Michele Ceriotti, head of EPFL’s Laboratory of Computational Science and Modelling, have taken a step in that direction with a new ML framework for predicting the electronic density of states (DOS). The technique has already been applied to understand electronic transitions in dense amorphous silicon, in a paper that came out in Nature today. 

The NCCR MARVEL has ‘Materials’ revolution’ in its name for a reason: it seeks to radically transform and accelerate the process of materials discovery and design. It seeks to do this by taking a computational approach, built on a platform of database-driven, high-throughput quantum simulations: if we could compute the properties of every material using electronic structure calculations, and if we could use that information to screen big databases for materials with just the right characteristics for a given purpose — then that would put us at an enormous advantage in the search for new and better materials, avoiding much of the trial and error that comes with experiments in the lab. Such a project requires robust computer resources. For one, the sort of calculations that MARVEL research relies on take a lot of computing time and data storage; as those calculations get ever more sophisticated, demands on computing power will only grow. For another, to turn the MARVEL project into a true revolution, computer resources must be organised in such a way that they can serve as a platform for the broader materials science community.

Using state-of-the-art density-functional perturbation theory and the Boltzmann transport equation, researchers led by Prof. Nicola Marzari, head of THEOS and NCCR MARVEL, investigated monolayer materials with outstanding transport properties to identify several high-conductivity materials. While some have only recently been discussed in the literature, others have never been presented in this context. Comparing the 11 monolayers in detail allowed the researchers to investigate how the strength and angular dependency of electron-phonon scattering drives key differences in transport performance. It also allowed them to show the limitations of selecting potentially interesting materials based on band properties alone.

In the paper “Evaluation of Photocatalysts for Water Splitting through Combined Analysis of Surface Coverage and Energy-Level Alignment,” a team of MARVEL researchers led by Alfredo Pasquarello, head of EPFL’s Chair of Atomic Scale Simulation have used computational analysis to identify a promising catalyst candidate for potential use in water splitting.  

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.

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.

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. 

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.