MARVEL has transformed materials research by combining simulations, machine learning, and experiments to predict and design novel materials. Its open-source codes and computational infrastructures have strengthened the field, enabling reproducible and collaborative discoveries.
Researchers have introduced QuaTrEx, a software package that combines materials modeling methods to simulate the behavior of nanoribbons made of over 42,000 atoms. This breakthrough enables the simulation of realistic electronic devices, paving the way for improved performance in next-generation transistors.
A new dataset and model improve the efficiency of machine-learning interatomic potentials and their applicability to different chemical elements and material classes. The PET-MAD model uses a compact and denser dataset of 95,595 structures and an original neural network architecture, achieving robust simulations with minimal fine-tuning.
Researchers develop new method to simulate Pockels effect, a key phenomenon in optoelectronics, using Density Functional Theory and finite differences. The approach enables accurate modeling of barium titanate's behavior, paving the way for more efficient devices.
A new study uses machine learning to reduce time needed for calculating screening parameters in Koopmans functionals, enabling faster predictions of material spectral properties. Researchers trained a simple model using modest data and achieved accurate results, paving the way for studying temperature-dependent spectral properties.
Researchers used computational methods to screen potential plasma-facing materials for fusion reactors, considering factors like thermal resistance and neutron bombardment. A shortlist of 21 materials was identified, including tungsten, diamond, and tantalum nitride, which showed promise for divertor applications.
A new benchmark, V-score, compares performance of classical and quantum algorithms in simulating complex phenomena in condensed matter physics. The study identifies the hardest problems in materials science, including frustrated geometries and strong electron interactions.
A team of scientists successfully interfaced AiiDA with the Alps supercomputer, completing over 100,000 calculations in just 16 hours. The run demonstrated the maturity of Swiss-made software tools for computational materials science and showcased the power of Switzerland's main supercomputing facility.
Researchers at NCCR MARVEL have discovered a chain of copper and carbon atoms that forms the thinnest metallic nanowire stable at 0K. CuC2 has promising properties for flexible electronics, including its ability to be bent without losing its metallic behavior.
Researchers integrated AiiDA with tomato to control battery cycling, allowing for automated experiments and data collection. The integration enables batch submission of protocols, provenance tracking, and analysis.
Researchers noticed a pattern where 60% of materials have primitive unit cells made up of a multiple of four atoms. Despite analyzing various factors, they were unable to find an explanation for the 'Rule of Four', a phenomenon that has been observed in two widely used databases.
Researchers have discovered that magnetostriction causes a magnetic phase transition in manganese oxide at 118K, leading to the switch of muon sites. The study uses advanced simulations and resolves a long-standing puzzle, shedding new light on antiferromagnetic oxides.
A team of scientists from NCCR MARVEL has carried out the most comprehensive verification effort on solid-state DFT codes, simulating 960 materials and their properties using two independent state-of-the-art codes. The study provides a reference dataset and guidelines for assessing and improving existing and future code, ensuring repro...
Researchers at NCCR MARVEL found that EuCd2As2 behaves as a magnetic semiconductor with intermediate electrical conductivity, contrary to predictions of its Weyl semimetal properties. The study's use of optical spectroscopy highlights the importance of this technique in understanding material behavior.
A new approach recovers the elastic tensors and moduli of superionic materials through first-principles molecular dynamics simulations. This resolves a significant overestimation issue with static methods, providing accurate reference results for three benchmark materials.
The Psi-k conference is a premier event for researchers in computational materials science, attracting experts from 50 countries. The conference features 38 symposia, 115 invited talks, and 250 contributed talks on topics ranging from materials discovery to emerging computing.
A novel quantum simulation method clarifies the correlated properties of complex material 1T-TaS2. The study reveals that the insulating behavior stems from a complex interplay between bonding-antibonding splittings and electronic correlation.
AV3Sb5 kagome metals exhibit unusual quantum phenomena such as high-temperature superconductivity. Researchers identified four Van Hove singularities near the Fermi level, which enhance correlation effects and lead to competing orders.
The study uses many-body perturbation theory to predict the optical properties of negatively charged boron vacancies in hBN, showing that phonons are largely responsible for luminescence. The results suggest that this defect can be used as a nanoscale thermometer with high temperature sensitivity.
Researchers at NCCR MARVEL identified two new cubic prototypes that exhibit energetically and dynamically stable paraelectric behavior, providing a microscopic representation of the material's properties. The discovery has significant implications for the study of ferroelectricity, superconductivity, and other functional materials.
The NCCR MARVEL has been ranked in the top tier by the SNSF Research Council, receiving a bonus of 1.53 MCHF in addition to its regular budget. The review panel praises MARVEL's scientific excellence and added value, highlighting its expected high impact during phase III.
Researchers successfully synthesized nitrogen-substituted undecacenes using on-surface chemistry, retaining their electronic properties with modified orbital energies. The study offers a new method for investigating complex electronic correlation effects in acenes and developing organic electronics and spintronics.
Researchers use ARPES to study quasi-one-dimensional metallic TaSe3 and observe multiple mobile excitons manifested as sidebands. The excitons have different internal structures depending on the involvement of holes and electrons from the same chain or neighboring ones.
Researchers develop a symmetrized N-center representation that provides a natural, fully equivariant framework for learning properties associated with multiple atoms. The approach gives excellent accuracy for predicting atomic properties despite using only linear or kernel regression.
Researchers found that two commonly used atomic fingerprints, ACSF and SOAP, are insensitive to certain movements, leading to the failure of machine learning in resolving four-body interactions. This limitation affects the accuracy of reproducing these interactions with limited success.
Researchers developed a machine learning method to assign NMR spectra of organic crystals probabilistically from their 2D chemical structures. The approach uses a database of chemical shifts for organic solids, reducing computational cost by up to 10,000 times compared to current methods.
Researchers at NCCR MARVEL identified lutetium oxide iodide (LuIO) as a high-performance material for spin-layer-locking spinFETs. They demonstrated the control of its properties with electric gates, providing practical guidelines for building and operating devices from this material.
Researchers have developed a common workflow interface for various quantum codes, enabling accurate predictions of system properties and promoting the wider use of density-functional theory. The interface allows users to optimize structures using any code without defining parameters, providing reusable results.
The OPTIMADE API specification allows for seamless access and interoperability across multiple materials databases, including AiiDA and Materials Cloud. Researchers can query databases, expose links between them, and deliver standardized results using the OPTIMADE API.
The article reviews electronic-structure methods for materials design, discussing their capabilities, limitations, and potential applications. The authors highlight the importance of combining simulations with experiments and emphasize the need for advanced computational infrastructure to support these efforts.
Graphene nanoribbons exhibit structural disorder due to missing carbon atoms, known as 'bite' defects. These imperfections degrade electronic device performance but offer promising opportunities for spintronic applications with unique magnetic properties.
A combined molecular dynamics and experimental study reveals a two-step process that enables the formation of phase-pure α-FAPbI3 at lower temperatures. The researchers used metadynamics to simulate the transformation from PbI2 to perovskite, which was confirmed by in situ x-ray and thin-film experiments.
Researchers have discovered a high-temperature superconductor in the 2D material W2N3, with a critical temperature of 21 K. This finding has significant implications for the development of nanoscale devices and our understanding of topological properties.
A machine learning model permits full quantum description of the solvated electron, capturing its complex behavior and dynamics. The model revealed transient diffusion, a rare event not present in classical simulations.
Researchers developed a machine learning model to predict electronic density of states (DOS) for materials properties. The model demonstrates transferability across different phases and scalability to large system sizes, making it applicable to address long-standing open questions in materials science.
Researchers used artificial neural networks to simulate hydrogen's phase transitions at high pressures and temperatures, challenging previous assumptions. The study suggests a smooth transition between insulating and metallic layers in giant gas planets, reconciling existing discrepancies between lab and modeling experiments.
Researchers at ETH Zurich and EPF Lausanne have identified 13 possible 2D materials that can be used to build ultra-scaled field-effect transistors, potentially surpassing conventional silicon-based technology.
Researchers have identified jacutingaite as a dual-topological insulator, exhibiting both weak and topological crystalline insulator properties. The material's dual nature is attributed to strong interlayer hybridization leading to a novel hopping term, resulting in protected surface states.
Researchers developed a new MOF co-catalyst that selectively produces branched aldehydes, which are difficult to achieve with existing catalysts. The study demonstrates the potential of MOFs to enhance selectivity in various catalytic reactions.
A new artificial neural network model has been developed to solve inverse problems, demonstrating accuracy comparable to the maximum entropy (MaxEnt) approach. The model's versatility and robustness against noisy data have been showcased in various tests, including recovering electron single-particle spectral densities.
Researchers at MARVEL have generalized Fourier's heat equation, explaining hydrodynamic heat propagation in materials. The new formulation yields results that agree with experimental results on graphite and predicts the possibility of observing hydrodynamic heat transfer in diamond at room temperature.
The Wannier90 program has transitioned to a community-driven model, featuring new functionalities, improvements, and features. The 3.0 release includes methods for WFs calculation, parallelization, interfaces with new codes, and user functionality.
A new microscopic theory describes heat transport in general ways, applying to ordered or disordered materials like crystals or glasses. The equation allows accurate prediction of thermoelectric material performance, which is crucial for efficient energy conversion and cooling.
Researchers have developed a self-healing catalyst, SION-X, that can efficiently release hydrogen from ammonia borane, a promising energy carrier. The catalyst is based on abundant mineral Jacquesdietrichite and can be easily regenerated, stored, and handled, making it suitable for large-scale applications.
Researchers used correlated wavefunction theory to simulate the bulk hydrated electron, finding a persistent tetrahedral cavity made up of four water molecules. The model provided stronger theoretical evidence for the cavity model, dismissing non-cavity structures in stable and metastable states.
Researchers discovered that applying mechanical pressure to tetraethylammonium di-iodine triiodide increases its conductivity. The pressure-induced changes lead to the formation of CT chains, making TEAI a tunable pressure-sensitive electric switch.
The researchers developed site-dependent +U correction parameters using self-consistent first principles calculations, which improves the structure of stoichiometric SrMnO3 and provides a more accurate description of defective systems. This approach can lead to lower computational cost and more precise predictions of defect energetics.
A new machine learning methodology was developed by researchers at NCCR MARVEL to capture chemical intuition from partially failed trials. This approach helps chemists improve their synthesis conditions and create novel materials with higher surface areas.
The NCCR MARVEL/CECAM team has been awarded the EPFL Open Science Fund to develop open software services for classrooms and research. The team will create an online hub with simulation and data-analysis tools that can be easily used by researchers and students.
Marzari's group develops open-source solutions to link proprietary IBM technologies with AI models, identifying promising materials for further study. The project enables automatic calculation of material properties on demand, highlighting outliers worthy of experimental characterization.