Modeling & Simulation

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More

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.

Read More