• Computational Model Paves the Way for More Efficient Energy Systems

    EPFL researchers make theoretical breakthrough in thermoelectric material to better harness waste heat for sustainable energy.

  • Over 3,000 bidimensional materials are now in the Materials Cloud database

    The collection of 2D materials, first initiated  in 2018, has been expanded with 1,252 new monolayers that could be exfoliated from existing tridimensional structures.

  • A compass to explore covalent organic frameworks in search of good photocatalysts.

    A new study by Berend Smit’s group at EPFL introduces a new computational framework that allows to screen large numbers of Covalent Organic Frameworks (COFs) in a fast and efficient way, to pre-select the best candidates for specific photocatalytic applications, such as water splitting. Starting from a set of 419 COFs for which there are reported experiments, the workflow allowed the selection of 13 candidate materials for water splitting. 

  • Can quantum computers be the key to more natural and efficient modelling of materials?

    Quantum computing holds great promises for computational material science. A new project added to NCCR MARVEL at the beginning of Phase III wants to explore the potential of the current generation of hybrid systems - that combine a classical machine with a quantum one – for the simulation of electronic structures. Project leader Giuseppe Carleo explains how the group will develop new algorithms and novel machine learning strategies to run on these machines and improve the accuracy of current modelling methods. “The classical part does the heavy lifting and brings you very close to the exact solution," he explains, "and then you hope the quantum part gives you the extra step to reach the final accuracy".

  • Using alchemical compression and machine learning to describe high-entropy alloys

    A new method developed by the NCCR MARVEL laboratory of Michele Ceriotti at EPFL allows modelling alloys containing up to 25 transition metals, matching a remarkable accuracy with a manageable requirement of data and computational resources. The group validated the model and used it for computational experiments on three representative alloy compositions. In the future the model will be expanded to predict the catalytic behaviour of the surfaces of high-entropy alloys.

  • Crucial role of inter-site Hubbard interactions for the correct energetics of spinel Li-ion cathode materials

    NCCR MARVEL researchers have applied DFT with extended Hubbard functionals (DFT+U+V) to the study of two candidate cathode materials belonging to the class of lithium-manganese-oxide spinels. They used a new approach to determine the Hubbard U and V parameters entirely from first principles and found that the method accurately predicts the geometrical properties, oxidation states, band gaps, and voltages of the two materials. The article was included in the "2023 PCCP HOT Articles" collection.

  • Temperature dependence of energy band gap suggests ZrTe5 is a weak topological insulator

    Researchers including NCCR MARVEL’s Professor Ana Akrap at the Department of Physics at the University of Fribourg used Landau-level spectroscopy to determine the temperature dependence of the energy band gap in zirconium pentatelluride (ZrTe5). They found that the band gap is non-zero at low temperatures and increases monotonically when the temperature is raised, adding to evidence that ZrTe5 is a weak topological insulator. 

  • Graph neural network parametrized potentials describe intermolecular interactions

    An ETHZ team led by Prof. Sereina Riniker, Associate Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences, has developed an alternative strategy for parametrizing intermolecular interactions. Described in the paper “Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions,” recently published in the Journal of Chemical Theory and Computation, the approach accelerates and simplifies the parametrization process of classical force fields and can take advantage of large data sets. Used in combination with machine learning-based techniques, the model allows researchers to take advantage of the best of both, and access a universal optimization toolkit combined with robust and physically constrained models.

  • Solids that are also liquids: elastic tensors of superionic materials

    NCCR MARVEL researchers have applied for the first time first-principles molecular dynamics with the Parrinello-Rahman method to characterize the elastic properties of superionic conductors. Their study highlights the importance of the quasi-liquid dynamics of the Li ions in the elastic response, and showcases a significant softening of the predicted moduli compared to previous static approaches. The approach, complemented with the computation of the statistical errors, provides accurate information and reference results for the elastic constants and moduli of three benchmark oxide and sulfide solid-state electrolytes, and paves the way for a better understanding of the mechanical properties of the materials that may be used in the next generation of solid-state-battery technologies.

  • Theory-guided design identifies single-phase high entropy alloys with appealing properties

    Prof. William Curtin, leader of NCCR MARVEL’s Pillar 1, Design and Discovery of Novel Materials and member of the executive committee and colleagues including Dr. Christian Leinenbach, Head of the Advanced Processing & Additive Manufacturing of Metals at Empa have used theories and expanded thermodynamic tools to sift through a huge materials space to identify high entropy alloys that satisfy a range of necessary performance properties. They validated the approach against data from recent literature, identified a promising new material, fabricated it at Empa, and then confirmed several predicted properties through experiment. The method is general and can be adapted to to other performance requirements, making it a reliable and efficient means of discovery for the next generation of high-temperature alloys. 

  • Ceriotti’s perspective on integrated ML models for materials published in MRS Bulletin

    NCCR MARVEL’s Michele Ceriotti, EPFL professor and head of the School of Engineering’s Laboratory of Computational Science and Modelling, has published a perspective, “Beyond potentials: Integrated machine learning models for materials,” in the October edition of the MSR Bulletin.  

  • DFT+U+V for accurate electronic properties of olivine-type Li-ion cathode materials

    Understanding at the atomistic level how the properties of transition-metal elements lead to efficient electrochemical processes is critically important in the development of new cathode materials for lithium-ion batteries. While density-functional theory (DFT) calculations with local and semilocal exchange-correlation functionals can play an important role in producing first-principles predictions for these materials, they can also produce unsatisfactory results because of self-interaction errors. In the paper “Accurate Electronic Properties and Intercalation Voltages of Olivine-Type Li-Ion Cathode Materials from Extended Hubbard Functionals,” recently published in Physical Review X Energy, NCCR MARVEL researchers and colleagues carried out a comparative study of four electronic-structure methods for selected olivine-type cathode materials. They found that the DFT+U+V method clearly outperforms the others and is able to describe the interactions accurately, opening the door for the study of more complex cathode materials as well as for a reliable exploration of the chemical space of compounds for Li-ion batteries.