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
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
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
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
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
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
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
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
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
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