Modeling & Simulation

Accounting for nonlinearity—constitutive relations improve modeling of fiber-reinforced polymer composites

By Lisa McDonald / October 8, 2021

Modeling the mechanical behavior of fiber-reinforced polymer composites is difficult because of their nonlinear response to external stimuli. Constitutive relations offer one way to account for the nonlinearity, and a team of researchers in Russia used this approach to model polymer composites designed as shut-off valves for pressure vessel service equipment.

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Neural network speeds up identification of piezoelectric properties

By Lisa McDonald / August 17, 2021

Modeling is a good way to evaluate the performance of new piezoelectric materials without conducting costly experiments. Two researchers from University of the Republic in Uruguay explored using a neural network to speed up the modeling process.

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Toward next-gen electronics—machine learning framework accelerates exploration of how strain affects semiconductor properties

By Lisa McDonald / July 27, 2021

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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More than just a PHASE—thermodynamic and kinetic data and modeling help researchers develop materials of the future

By Jonathon Foreman / July 9, 2021

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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Determine oxidation stability of materials at MAX speed

By Lisa McDonald / April 23, 2021

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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Glass discovery and design: 21 challenges in artificial intelligence and machine learning for glass science

By Lisa McDonald / February 23, 2021

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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Manipulate light on the nanoscale: Proposed quantum dot–graphene scheme improves conversion of light into surface waves

By Lisa McDonald / December 11, 2020

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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Dynamic geometric modeling allows fabrication of complex-shaped ceramic bone implants

By Lisa McDonald / December 8, 2020

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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Modeling ceramic conduction: Researchers update small-polaron transport model to account for complex oxide systems

By Lisa McDonald / October 30, 2020

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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Machine learning model predicts superhard materials from crystal structure

By Lisa McDonald / September 25, 2020

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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