Highlights

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. 

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.

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.

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. 

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.  

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.

Simone Gallarati and a team led by Professor Clemence Corminboeuf, head of the Laboratory for Computational Molecular Design at EPFL, have created a dataset containing thousands of organic molecules that have been mined from the literature and from the Cambridge Crystallographic Database, and enriched with species generated in a combinatorial fashion by recombining molecular building blocks.  The result is a map that could help researchers navigate organocatalyst space and enable informed catalyst design. It’s also the starting point for a multitude of fragment-based reaction optimization methods. 

Zeolites are nanoporous frameworks that can be applied in a number of industrial processes, particularly in separation and catalysis. While much effort has been put into identifying and synthesizing new zeolite structures, success has been largely theoretical. While massive databases of hypothetical zeolites have been generated, containing millions of new framework structures, none has been made in the lab. In the paper “Ranking the synthesizability of hypothetical zeolites with the sorting hat,” NCCR MARVEL’s Michele Ceriotti, EPFL professor and head of the School of Engineering’s Laboratory of Computational Science and Modelling, and colleagues present a data-driven procedure for distinguishing known structures from hypothetical ones and categorizing them into compositional classes. The approach helps identify promising synthetic candidates and suggests likely chemical compositions, providing a sort of recipe for materials chemists.

A paper describing the Open Software Services for Classrooms and Research (OSSCAR) platform has recently been published in the journal Computer Physics Communications. Developed through a collaboration between NCCR MARVEL and the Centre Européen de Calcul Atomique et Moléculaire (CECAM), OSSCAR provides an open collaborative environment for developing and accessing educational resources through web applications. Associated tools are easy to use, and create a uniform and open environment that can be used by a large academic community and are meant to facilitate learning and help people avoid duplicating efforts in the creation of teaching material. Contributions to expand the educational content of the OSSCAR project are welcome.

Polarons can affect numerous phenomena in a material but have been difficult to model correctly. In the paper “Many-Body Self-Interaction and Polarons,” recently published jointly in Physical Review Letters and in Physical Review B as an Editor’s Suggestion, researchers Stefano Falletta and Professor Alfredo Pasquarello of the Chair of Atomic Scale Simulation at EPFL advance the conceptual understanding of the self-interaction problem in density functional theory, paving the way to efficient calculations of polarons in large systems, in systematic studies involving large sets of materials, and in molecular dynamics evolving over long time periods.

Transition-metal-based “kagome” materials, referred to as such because of the resemblance of their structure to a Japanese woven bamboo pattern, are rich platforms for investigating novel topological electronic states and correlated phenomena. Until now, topologically nontrivial Dirac surface states (TDSSs) have not been definitively observed in the idealized 2D kagome lattice, nor has the question of whether such states or van Hove singularities (VHSs) can be tuned been explored extensively. In the paper “Tunable Topological Dirac Surface States and Van Hove Singularities in Kagome Metal GdV6Sn6,” recently published in Science Advances, researchers led by Professor Ming Shi at the Paul Scherrer Institute address both topics, identifying and manipulating these features in the magnetic kagome material GdV6Sn6. The results establish the material as an excellent platform for studying the nontrivial topology, magnetism and correlation effects and open a new avenue of potential development for use in spintronic devices.

Applying quantum chemistry (QC) approaches to the high-throughput screening of crystallographic data repositories could enable huge advances in molecular and materials design. The challenge, however, particularly in transition metal complexes, is to retrieve all the information needed to perform QC computations. In the paper "cell2mol: encoding chemistry to interpret crystallographic data," recently published in Nature Publishing's journal Computational Materials, Sergi Vela and the team led by Professor Clémence Corminboeuf, head of the Computational Molecular Design Laboratory at EPFL, present a novel solution. A fully automatic pipeline for characterizing molecular crystals, cell2mol greatly simplifies the construction of QC-ready datasets. The authors show that the code can interpret the large chemical diversity and structural complexity contained in crystallographic repositories by simply encoding the chemist's view when visualizing a crystal structure. Under this perspective, the unit cell is understood by the hierarchy of molecular fragments, and their Lewis structures and formal charges. The code, as well as reliable QC-ready databases of transition metals with incomparable chemical diversity, are now available.