Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Dokumenttyp
Sprache
- Englisch (7)
Referierte Publikation
- ja (7) (entfernen)
Schlagworte
- DFT (4)
- Automation (3)
- High-throughput (3)
- Chemical bonds (2)
- Phonons (2)
- Bonding Analysis (1)
- Database (1)
- Diffusons (1)
- Fracture surface energy (1)
- Glass (1)
Organisationseinheit der BAM
- 6 Materialchemie (6)
- 6.0 Abteilungsleitung und andere (6)
- 5 Werkstofftechnik (1)
- 5.6 Glas (1)
Silicate glass fracture surface energy calculated from crystal structure and bond-energy data
(2023)
We present a novel method to predict the fracture surface energy, γ, of isochemically crystallizing silicate glasses using readily available crystallographic structure data of their crystalline counterpart and tabled diatomic chemical bond energies, D0. The method assumes that γ equals the fracture surface energy of the most likely cleavage plane of the crystal. Calculated values were in excellent agreement with those calculated from glass density, network connectivity and D0 data in earlier work. This finding demonstrates a remarkable equivalence between crystal cleavage planes and glass fracture surfaces.
An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.
A recent approach to measure electron radiation doses in the kGy range is the use of phosphors with an irradiation dose-dependent luminescence decay time. However, the applicability of the previously investigated material NaYF4:Yb3+,Er3+ is limited as it shows pronounced fading. Therefore, in this work, a modified SrF2 synthesis is presented that results in SrF2 nanoparticles codoped with Yb and either Er, Hm, or Tm. To assess their suitability as dosimeter material, dose response, as well as its degree of fading over 50 up to 140 days after irradiation were measured. Fading rates as small as 5% in SrF2:Er,Yb and 4% in SrF2:Ho,Yb were derived, which are comparable to established dosimeter materials. A combination of spectroscopy, diffraction and DFT calculations was used to elucidate the effect of irradiation, pointing towards the formation of a secondary phase of Yb2+ that we predict could be Yb2OF2. This irreversible formation of a secondary phase is considered to be the explanation for the low fading behavior in SrF2-based phosphors compared to NaYF4:Yb, Er, a highly attractive feature for electron beam dosimetry.
Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11.
Next-generation thermal management requires the development of low lattice thermal conductivity materials, as observed in ionic conductors. For example, thermoelectric efficiency is increased when thermal conductivity is decreased. Detrimentally, high ionic conductivity leads to thermoelectric device degradation. Battery safety and design also require an understanding of thermal transport in ionic conductors. Ion mobility, structural complexity, and anharmonicity have been used to explain the thermal transport properties of ionic conductors. However, thermal and ionic transport are rarely discussed in direct comparison. Herein, the ionic conductivity of Ag+ argyrodites is found to change by orders of magnitude without altering the thermal conductivity. Thermal conductivity measurements and two-channel lattice dynamics modeling reveal that the majority of Ag+ vibrations have a non-propagating diffuson-like character, similar to amorphous materials. It is found that high ionic mobility is not a requirement for diffuson-mediated transport. Instead, the same bonding and structural traits that can lead to fast ionic conduction also lead to diffuson-mediated transport. Bridging the fields of solid-state ionics and thermal transport, it is proposed that a vibrational perspective can lead to new design strategies for functional ionic conducting materials. As a first step, the authors relate the so-called Meyer-Neldel behavior in ionic conductors to phonon occupations.
Automation simplifies the use of computational materials science software and makes it accessible to a wide range of users. This enables high-throughput calcula-tionsand makesiteasier for non-specialists to enter computational materials science. However, in-creasing automation also poses threats that should be considered while interacting with automated procedures.