Modeling & Simulation

One phase to rule them all—researchers use Monte Carlo simulations to determine most stable structure of boron nitride

By Lisa McDonald / April 29, 2022

Though hexagonal boron nitride is generally regarded as the most stable boron nitride structure, the relative phase stabilities of boron nitride polymorphs are still under debate. Researchers led by the Japan Advanced Institute of Science and Technology used Monte Carlo simulations to calculate the relative phase stabilities of these polymorphs.

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Let there be (controlled) light—new calibration method eliminates illumination variations in scans of stained-glass windows

By Lisa McDonald / April 1, 2022

Hyperspectral imaging has gained much attention in the field of cultural heritage, but there are difficulties using it outdoors due to ever-changing levels of light. Researchers looked to overcome this limitation by developing a calibration method that can account for and eliminate variations in illumination.

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New analysis model determines reliability and sensitivity of cold-bent curtain wall glass

By Lisa McDonald / February 8, 2022

Cold bending is a method commonly used to form curved glass for curtain walls. Researchers in China developed an analysis model to determine the reliability and sensitivity of glass formed this way.

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Probing the ocean’s origins—ultrahigh-pressure magnesium hydrosilicates may have served as reservoirs of early water

By Lisa McDonald / February 4, 2022

The origins of the world ocean remain a much-debated topic to this day. A new paper by researchers from several universities in China and the Skolkovo Institute of Science and Technology in Russia posits that magnesium hydrosilicates served as reservoirs of water in early Earth.

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Challenging the standard—researchers propose new model for determining piezoelectricity in ferroelectric crystals

By Lisa McDonald / December 14, 2021

When designing ferroelectric materials, researchers have long been guided by the belief that smaller domain sizes lead to greater piezoelectric properties. A recent study by Penn State and Xi’an Jiaotong University researchers raises questions about this standard rule.

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Materials Genome Initiative updates strategic plan

By Eileen De Guire / December 10, 2021

In November 2021, the National Science and Technology Council subcommittee marked the 10th anniversary of the Materials Genome Initiative by issuing a new, comprehensive five-year strategic plan for the MGI. Learn more about the new plan in today’s CTT.

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Periodic improvements—new neural network demonstrates enhanced symmetry awareness

By Lisa McDonald / November 2, 2021

Current neural networks are incapable of understanding symmetry, which limits the conclusions that can be drawn from the data. Researchers from Lehigh and Stanford universities developed a new model that includes symmetry-aware features to improve symmetry approximation.

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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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