Prediction-of-semiconducting-clathrate-materials

Accurate Prediction of the Thermal Conductivity for All Classes of Materials

In the recently accepted Physical Review Letter, Christian Carbogno, Rampi Ramprasad, and Matthias Scheffler derive a novel, accurate, and unique first-principles description of heat transport in solid materials...
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CO2 Conversion to Fuels and Other Useful Chemicals

Reducing air pollution and finding renewable energy sources are among the most important challenges of our time.
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Uncovering Structure-Property Relationships of Materials by Subgroup Discovery

Finding descriptors of materials from high-dimensional data using human-intuition is difficult and often subjective.
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Layered van der Waals Crystals with Hyperbolic Light Dispersion

Finding descriptors of materials from high-dimensional data using human-intuition is difficult and often subjective.
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Shell-NOMAD Collaboration Creates a Virtual Reality Experience for Materials Design

Shell Technology Center Bangalore, the 3rd technology hub for Royal Dutch Shell after Amsterdam and Houston, was inaugurated on 31st March, 2017. The inauguration saw the presence of distinguished guests from Indian Industry, Academia, Government and senior leadership of Shell coming together.
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Transparent n-type (anode) conducting materials

Transparent conductors are an important class of materials that are electrically conductive with low absorption over the visible range of the electromagnetic spectrum
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Prediction of semiconducting clathrate materials: a promising finding for thermoelectricity

Maria Troppenz, Santiago Rigamonti and Claudia Draxl have studied the stability and electronic properties of clathrate compounds Ba8AlxSi46–x and Sr8AlxSi46–x over a wide range of Al content, employing a novel cluster-expansion technique.
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Exploitation of large materials databases relies on effective ways of comparing molecular and crystalline structures

Large databases of materials and molecules are increasingly available, due in part to maturation of electronic structure software and in part to cheap computing, but also advances in our ability to exploit them, using modern machine learning tools and similar data-driven approaches. Many of these methods rely on effective ways to compare individual entries in the database, i.e. having a measure of similarity between crystal structures or molecules. Many such measures have been proposed and used for various purposes in the past, and typically they are ad-hoc, tailor-made for a particular application. Often such similarity measures are not "complete" and cannot be made complete systematically, in the sense that non-identical data items are deemed equivalent.
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