Elastic strain engineering is an emerging technique for enhancing the performance of functional materials. An international collaboration involving Skoltech, MIT, and Nanyang Technological University developed an expanded machine learning framework to accelerate the exploration of how strain affects semiconductor properties.
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Thermodynamic and kinetic data and modeling can speed up the material design process immensely. Learn about two such techniques in today’s CTT—phase equilibrium diagrams and the CALPHAD method.
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Determining oxidation stability of new MAX phases is a difficult and expensive process with current computational and experimental methods. Researchers at Texas A&M University designed a new machine-learning-based scheme for predicting the oxidation of MAX phases at high temperatures, allowing them to conduct studies that may otherwise take years to perform.
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Artificial intelligence and machine learning approaches to materials design can accelerate the discovery of new glasses in an economical fashion. Researchers from the Indian Institute of Technology Delhi and the University of California, Los Angeles, identified 21 challenges that, when addressed, can aid in harnessing the full potential of these methods for glass science.
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Surface plasmon polaritons are a type of surface wave that, when harnessed, show potential to improve various processes that take place on the nanoscale, such as molecular imaging. Researchers from two places in Russia propose a new scheme using quantum dots and graphene to more efficiently convert light into surface plasmon polaritons for use in such applications.
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Conventional CAD modeling of ceramic bone implants is limited in the structures that it can design. Researchers at Skolkovo Institute of Science and Technology in Russia explored using function representation modeling instead to expand the design possibilities.
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To describe electronic charge transport in oxides, researchers rely on a small-polaron transport model that was developed six decades ago for binary oxides rather than higher-order systems. Researchers from Cornell University and Technion–Israel Institute of Technology have now updated the model with additional parameters to more accurately model complex oxide systems.
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Conventionally, theoretical models are unable to predict a material’s hardness from its crystal structure because the underlying physical principles are complex. A new machine learning model developed by two researchers at Skolkovo Institute of Science and Technology succeeds in making such predictions in a fast and reliable manner.
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Modeling offers a way to learn about ancient ceramics without damaging the priceless items. Two recent articles in International Journal of Ceramic Engineering & Science illustrate how modeling provides insights into myriad properties, including mechanical behaviors and coloring mechanisms.
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Understanding the atomic structure of glass and other amorphous materials is difficult because, unlike crystals, the structure only consists of short-range and medium-range order; long-range order is absent. Researchers led by Aalborg University demonstrate how a topological method called persistent homology could help reveal a glass’s medium-range order structural features.
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