The May 2022 issue of the ACerS Bulletin—featuring past, present, and future applications of glass—is now available online. Plus—National Day of Glass recap.
Read MoreDetermining 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.
Read MoreArtificial 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 MoreConventionally, 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 MoreMachine 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 MoreMachine learning can greatly facilitate design of new glasses by predicting a range of promising compositions to test. A recent paper by researchers from the University of California, Los Angeles, reviews studies investigating machine learning methods for just that purpose.
Read MoreNanoparticles may promote cancer metastasis, ceramic sensors for bridge strain, and other materials stories that may be of interest for February 13, 2019.
Read MoreAn interdisciplinary group of scientists at the University of Pennsylvania have harnessed intense computation, data, and modeling power to determine how disordered solids fail, an understanding that could help engineer custom materials, such as glass that is less likely to break.
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