Open Access

Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties.One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved

Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid‐state materials. However, integrating bonding information from these frameworks into machine learning pipelines at scale has been limited by the lack of a systematically generated and validated database. Recent advances in high‐throughput bonding analysis workflows have addressed this issue, and our previously computed Quantum‐Chemical Bonding Database for Solid‐State Materials was extended to include approximately 13,000 materials. This database is then used to derive a new set of quantum‐chemical bonding descriptors. A systematic assessment is performed using statistical significance tests to evaluate how the inclusion of these descriptors influences the performance of machine‐learning models that otherwise rely solely on structure‐ and composition‐derived features. Models are built to predict elastic, vibrational, and thermodynamic properties typically associated with chemical bonding in materials. The results demonstrate that incorporating quantum‐chemical bonding descriptors not only improves predictive performance but also helps identify intuitive expressions for properties such as the projected force constant and lattice thermal conductivity via symbolic regression.

Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In this work, we studied Ag8TS6 (T = Si, Ge, Sn) argyrodite family by combining chemical-bonding analysis, lattice vibrational properties simulation, and experimental measurements to investigate their structural and thermal transport properties. Furthermore, we propose a two-channel lattice-dynamics model based on Grüneisen-derived phonon lifetimes and compare it to an approach using machine-learned interatomic potentials. Both approaches are able to predict thermal conductivity in agreement with experimental lattice thermal conductivities along the whole temperature range, highlighting their potential suitability for future high-throughput predictions. Our findings also reveal a relationship between bond heterogeneity arising from weakly bonded Ag+ ions and occupied antibonding states in Ag–S and Ag–Ag interactions and strong anharmonicity, including large Grüneisen parameters, and low sound velocities, which are responsible for the low lattice thermal conductivity of Ag8SnS6, Ag8GeS6, and Ag8SiS6. We furthermore show that thermal and ionic conductivities in all three compounds are independent of each other and can likely be tuned individually.

Recently, machine learning of magnetic properties of transition metal compounds has attracted large interest due to environmental and availability issues of rare-earth-based functional magnetic materials. Surprisingly, bond-angle-derived features were not found to be relevant for magnetic structure prediction in previous studies using DFT-computed labels. This contrasts with a well-known magnetism heuristic, the Kanamori-Goodenough-Anderson (KGA) rules of superexchange. We review magnetic interaction trends within the MAGNDATA database of experimentally determined magnetic structures. Observed trends follow the KGA rules „of thumb“ and exceptions can be rationalized. We introduce a new, informative label for predicting magnetic structures that can be extended to magnetic sites and structures of arbitrary complexity. Bond-angle-derived features are found to be highly relevant for magnetic structure prediction.

Environmental and scarcity issues of common functional magnetic materials for, e.g., permanent magnets have intensified the search for rare-earth-free alternatives. This challenge is increasingly met by machine learning of magnetic properties of transition-metal compounds. Surprisingly, bond-angle-derived features were not found to be relevant for magnetic structure prediction in previous studies using DFT-computed labels. This contrasts with the Kanamori-Goodenough-Anderson (KGA) rules of superexchange, present in every magnetism textbook. These semiempirical rules predict whether a nearest-neighbor magnetic interaction in insulators is FM or AFM based on the bond angle, orbital symmetry, and orbital occupancy. For some cases, the rules can be simplified further to only consider the bond angle of neighboring magnetic sites (KGA rules of thumb). We review magnetism—bond angle trends within the MAGNDATA database, the largest collection of experimentally determined magnetic structures. Observed trends follow the KGA rules of thumb, and exceptions can be rationalized. In contrast, bond angles in a popular theoretical DFT database show very different trends and do not depend on the magnetic ordering. Building on our analysis, we engineer heuristic-derived features for the machine learning of magnetic structures. We introduce a new, informative label for predicting magnetic structures that can be extended to magnetic sites and structures of arbitrary complexity. We show that features derived from the heuristic are of high importance for this machine learning task. Beyond this, our model enables the prediction of non-collinear magnetic structures. Further, we analyze local and global structural trends of non-collinear magnets in the MAGNDATA database.