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What the World Needs Now Is Superhard Carbon

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Superhard materials are highly prized, ironically enough, for their flexibility. Not in terms of bending, but rather in terms of what they can be used to build. Creating scratch-resistant coatings, for example, could have any number of uses. So finding more of these materials is a priority for scientists, which is why a team from the University of Buffalo used artificial intelligence to identify 43 previously unknown forms of carbon that are thought to be stable and superhard. The 43 carbon structures are still theoretical, meaning that scientists have predicted them, but haven't actually brought them forward into creation yet.


New algorithm can more quickly predict LED materials

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Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host--the interaction between the two materials determines the performance.


New algorithm can more quickly predict LED materials: Researchers report machine learning speeds discovery of new materials

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They then synthesized and tested one of the compounds predicted computationally -- sodium-barium-borate -- and determined it offers 95 percent efficiency and outstanding thermal stability. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host -- the interaction between the two materials determines the performance.


New glass that is more than TWICE as hard as diamonds could be used to make bulletproof windows

Daily Mail - Science & tech

Diamonds, which are the hardest known substance, are typically used to cut glass into different shapes, but a new type of glass made of carbon is twice as hard as the precious gem. A team of Chinese scientists, led by those at Yanshan University, recently unveiled the transparent, yellow-tinted glass called AM-III, which is capable of leaving a deep scratch on a diamond. The material, which is made entirely of carbon, reached 113 gigapascals (GPa) on the Vickers hardness test, while diamonds typically score somewhere between 50 and 70 on the GPa scale. 'Consequently, our measurements demonstrate that the AM-III material is comparable in strength to diamond and superior to the other known strongest materials,' Professor Tian Yongjun of Yanshan University, who led the research, and his team noted in the study published in the journal National Science Review. AM-III, according to researchers, is not a diamond replacement, but could be used to develop stronger solar cells in solar panels and tougher bulletproof windows that would be 20 to 100 percent stronger than current models.


Predicting superhard materials via a machine learning informed evolutionary structure search

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The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because \(H_{\mathrm{v}} {{\mathrm{ML}}}\) values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XtalOpt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.