Modeling & Simulation

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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From mechanical behaviors to coloring mechanisms, modeling illuminates properties of ancient ceramics

By Jonathon Foreman / September 22, 2020

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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Persistence is key—topological data analysis reveals hidden medium-range order in glass

By Lisa McDonald / September 11, 2020

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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A double-edged sword—reverse engineered 3D-printed parts show security risk presented by machine learning

By Lisa McDonald / July 31, 2020

Machine learning is poised to play a big role in speeding up materials discovery and commercialization—but could such techniques present a risk to the global additive manufacturing market as well? Researchers at New York University showed they could potentially steal trade secrets by reverse engineering 3D-printed parts using machine learning.

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Modeling teaches old dogs new tricks: Viscosity predictions from dilatometry and DSC

By Jonathon Foreman / July 31, 2020

Determining viscosity of a glass through experiment is a slow and expensive process. In two recent papers published in JACerS, Penn State professor John Mauro and his colleagues show how it can be predicted much easier by using dilatometry and DSC to calculate parameters for a glass viscosity model that was proposed in 2009.

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Identify molecular ‘fingerprints’: Proposed graphene-based nanofocused sensor may improve molecular analysis

By Lisa McDonald / July 24, 2020

Mid-infrared spectroscopy is an important tool for nondestructive analysis of molecules, but it cannot analyze nanometric volumes very well. One way to improve nanometric analysis is through a technique called nanofocusing, and researchers in Spain and Russia proposed an improved nanofocusing technique using graphene.

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Data-driven approaches to materials challenges, plus more inside August 2020 ACerS Bulletin

By Lisa McDonald / July 16, 2020

The August 2020 issue of the ACerS Bulletin—featuring data-driven methods to augment experimental methods—is now available online. Plus—USGS Mineral Commodity Summaries

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Faster is not always easier—grain boundary diffusion of cations in fluorite and perovskite oxides

By Lisa McDonald / July 10, 2020

Fast grain boundary diffusion of cations is a well understood phenomenon in metals—but much less is known about this phenomenon in oxygen-ion conducting metal oxides. Researchers at RWTH Aachen University simulated this diffusion and found that although metal ions move faster along the boundaries than in bulk, the process is not necessarily less energy intensive.

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Deep learning provides deep help—researchers develop publicly available software for rational design of oxide glasses

By Lisa McDonald / May 5, 2020

Designing new oxide glass compositions can be an arduous process when relying on the “cook and look” approach. Researchers at the Indian Institute of Technology Delhi developed composition-property deep learning models for eight key oxide glass properties, and they made the software available publicly online.

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To infrared and beyond: Proposed quantum-based photodetector may expand spectral operating range

By Lisa McDonald / May 1, 2020

Since 2000, infrared photodetector technology has experienced rapid development—particularly quantum-based detectors. Now, researchers in Russia, Japan, and the United States developed a model for a detector that could operate in the far-infrared and even terahertz spectral ranges.

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