A5029698736- Machine Learning in Materials Science
- Advancements in Battery Materials
- Quantum Dots Synthesis And Properties
- Supramolecular Self-Assembly in Materials
- Surface Chemistry and Catalysis
- Advanced Sensor and Energy Harvesting Materials
- Supercapacitor Materials and Fabrication
- MXene and MAX Phase Materials
- X-ray Diffraction in Crystallography
- Advanced Materials and Mechanics
- Catalysis and Oxidation Reactions
- Electron and X-Ray Spectroscopy Techniques
- Fuel Cells and Related Materials
- Modular Robots and Swarm Intelligence
- Advanced Battery Technologies Research
- Extraction and Separation Processes
- Metal-Organic Frameworks: Synthesis and Applications
- Chemical Synthesis and Characterization
- Advanced Battery Materials and Technologies
- 2D Materials and Applications
- Inorganic Fluorides and Related Compounds
- Scientific Computing and Data Management
Technical University of Denmark
2020-2023
Nanotubes generated by rolling up transition metal dichalcogenide Janus monolayers are a new class of low-dimensional materials, which expected to display unique electronic properties compared their parent two- and three-dimensional structures. Here, we investigate the band structure $1H$-MoSTe armchair zigzag nanotubes, were recently hypothesized be stable as single-walled structures with radii only few nanometers. We first most nanotube sizes assess influence quantum confinement curvature...
The development of automated computational tools is required to accelerate the discovery novel battery materials. In this work, we design and implement a workflow, in framework Density Functional Theory, which autonomously identifies materials be used as intercalation electrodes batteries, based on descriptors like adsorption energies diffusion barriers. A substantial acceleration for calculations kinetic properties obtained due recent implementation Nudged Elastic Bands (NEB) method, takes...
Abstract In recent years, modeling and simulation of materials have become indispensable to complement experiments in design. High‐throughput simulations increasingly aid researchers selecting the most promising for experimental studies or by providing insights inaccessible experiment. However, this often requires multiple tools meet goal. As a result, methods are needed enable extensive‐scale with streamlined execution all tasks within complex protocol, including transfer adaptation data...
One-dimensional inorganic nanotubes hold promise for technological applications due to their distinct physical/chemical properties, but so far advancements have been hampered by difficulties in producing single-wall with a well-defined radius. In this work we investigate, based on Density Functional Theory (DFT), the formation mechanism of 135 different formed intrinsic self-rolling driving force found asymmetric 2D Janus sheets. We show that isovalent sheets, lattice mismatch between inner...
Understanding of interfaces in rechargeable batteries is crucial because they bridge electrodes, electrolytes, and current collectors. Current challenges that need to be overcome are reviewed, followed by future directions reach this goal.
Abstract Stable and fast ionic conductors for magnesium cathode materials have the prospect of enabling high energy density batteries beyond current Lithium‐ion technologies. So far, only a few candidate been identified leading to data being scarcely available community. Here, we present systematic study, in framework Density Functional Theory, including estimation migration barrier 16 through employing Nudged Elastic Band (NEB) calculations. By introducing path finder algorithm based on...
We have implemented a new reinforcement learning method able to rationally design unique metamaterial structures, which change shape during operational conditions. applied this nanostructured silicon anodes for Li-ion batteries.
The development of automated computational tools is required to accelerate the discovery novel battery materials. In this work, we design and implement a workflow, in framework Density Functional Theory, which autonomously identifies materials be used as intercalation electrodes batteries, based on descriptors like adsorption energies diffusion barriers. A substantial acceleration for calculations kinetic properties obtained due recent implementation Nudged Elastic Bands (NEB) method, takes...
The development of automated computational tools is required to accelerate the discovery novel battery<br>materials. In this work, we design and implement a workflow, in framework Density Functional<br>Theory, which autonomously identifies materials be used as intercalation electrodes batteries, based<br>on descriptors like adsorption energies diffusion barriers. A substantial acceleration for calculations<br>of kinetic properties obtained due recent implementation...
Abstract During the last decade, artificially architected materials have been designed to obtain properties unreachable by naturally occurring materials, whose are determined their atomic structure and chemical composition. In this work, we implement a new reinforcement learning (RL) method able design unique, rationally metamaterial structures at nano-, micro-, macroscale, which change shape during operational conditions. As an example, apply nanostructured silicon anodes for Li-ion...
Abstract Invited for this month's cover picture is the Section Atomic Scale Materials Modelling led by Prof. Tejs Vegge at Department of Energy Conversion and Storage, Technical University Denmark. The illustrates an autonomous workflow, implemented in framework Density Functional Theory, designed to automatically calculate crucial properties battery intercalation electrodes, including thermodynamic mechanical stability, open circuit voltages (OCV), as well kinetic barriers ionic transport....