Partitioning networks into communities of densely connected nodes is an important tool used widely across different applications, with numerous methods and software packages available for community detection. Modularity-based methods require parameters to be selected (or assume defaults) to control the resolution and, in multilayer networks, interlayer coupling. Meanwhile, most useful algorithms are heuristics yielding different near-optimal results upon repeated runs (even at the same parameters). To address these difficulties, we combine recent developments into a simple-to-use framework for pruning a set of partitions to a subset that are self-consistent by an equivalence with the objective function for inference of a degree-corrected planted partition stochastic block model (SBM). Importantly, this combined framework reduces some of the problems associated with the stochasticity that is inherent in the use of heuristics for optimizing modularity. In our examples, the pruning typically highlights only a small number of partitions that are fixed points of the corresponding map on the set of somewhere-optimal partitions in the parameter space. We also derive resolution parameter upper bounds for fitting a constrained SBM of
The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (
- Publication Date:
- NSF-PAR ID:
- 10042707
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 39
- Issue:
- 4
- Page Range or eLocation-ID:
- p. 191-202
- ISSN:
- 0192-8651
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract K blocks and demonstrate that these bounds hold in practice, further guiding parameter space regions to consider. With publicly available code (http://github.com/ragibson/ModularityPruning ), our pruningmore » -
Controlling nanoporosity to favorably alter multiple properties in layered crystalline inorganic thin films is a challenge. Here, we demonstrate that the thermoelectric and mechanical properties of Ca 3 Co 4 O 9 films can be engineered through nanoporosity control by annealing multiple Ca(OH) 2 /Co 3 O 4 reactant bilayers with characteristic bilayer thicknesses (b t ). Our results show that doubling b t , e.g. , from 12 to 26 nm, more than triples the average pore size from ∼120 nm to ∼400 nm and increases the pore fraction from 3% to 17.1%. The higher porosity film exhibits not only a 50% higher electrical conductivity of σ ∼ 90 S cm −1 and a high Seebeck coefficient of α ∼ 135 μV K −1 , but also a thermal conductivity as low as κ ∼ 0.87 W m −1 K −1 . The nanoporous Ca 3 Co 4 O 9 films exhibit greater mechanical compliance and resilience to bending than the bulk. These results indicate that annealing reactant multilayers with controlled thicknesses is an attractive way to engineer nanoporosity and realize mechanically flexible oxide-based thermoelectric materials.
-
Abstract Engineering semiconductor devices requires an understanding of charge carrier mobility. Typically, mobilities are estimated using Hall effect and electrical resistivity meausrements, which are are routinely performed at room temperature and below, in materials with mobilities greater than 1 cm2V‐1s‐1. With the availability of combined Seebeck coefficient and electrical resistivity measurement systems, it is now easy to measure the weighted mobility (electron mobility weighted by the density of electronic states). A simple method to calculate the weighted mobility from Seebeck coefficient and electrical resistivity measurements is introduced, which gives good results at room temperature and above, and for mobilities as low as 10−3cm2V‐1s‐1,
Here, μwis the weighted mobility, ρ is the electrical resistivity measured in mΩ cm, T is the absolute temperature in K,S is the Seebeck coefficient, andk B/e = 86.3 µV K–1. Weighted mobility analysis can elucidate the electronic structure and scattering mechanisms in materials and is particularly helpful in understanding and optimizing thermoelectric systems. -
Abstract The availability and easy access of large-scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, the lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein we first survey current materials informatics web apps and then propose and develop MaterialsAtlas.org, a web-based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including material’s composition and structure validity check (e.g. charge neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, and thermal conductivity), search for hypothetical materials, and utility tools. These user-friendly tools can be freely accessed at
http://www.materialsatlas.org . We argue that such materials informatics apps should be widely developed by the community to speed up materials discovery processes. -
Abstract Sb‐doped and GeTe‐alloyed n‐type thermoelectric materials that show an excellent figure of merit
ZT in the intermediate temperature range (400–800 K) are reported. The synergistic effect of favorable changes to the band structure resulting in high Seebeck coefficient and enhanced phonon scattering by point defects and nanoscale precipitates resulting in reduction of thermal conductivity are demonstrated. The samples can be tuned as single‐phase solid solution (SS) or two‐phase system with nanoscale precipitates (Nano) based on the annealing processes. The GeTe alloying results in band structure modification by widening the bandgap and increasing the density‐of‐states effective mass of PbTe, resulting in significantly enhanced Seebeck coefficients. The nanoscale precipitates can improve the power factor in the low temperature range and further reduce the lattice thermal conductivity (κlat). Specifically, the Seebeck coefficient of Pb0.988Sb0.012Te–13%GeTe–Nano approaches −280 µV K−1at 673 K with a low κlatof 0.56 W m−1K−1at 573 K. Consequently, a peakZT value of 1.38 is achieved at 623 K. Moreover, a high averageZT avgvalue of ≈1.04 is obtained in the temperature range from 300 to 773 K for n‐type Pb0.988Sb0.012Te–13%GeTe–Nano.
