Machine 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.
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The May 2018 issue of the ACerS Bulletin—featuring stories about how novel materials are overcoming limitations and opening new possibilities for glass optical fiber systems, beverage trends shaping the glass container industry, and much more—is now available online.
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Scientists at Corning Inc. (Corning, N.Y.) and Aalborg University (Aalborg, Denmark) have turned to computer modeling to help develop a glass-specific genome that will allow exploration and tailoring of specific properties of functional glasses.
Read MoreVolume 6 Issue 6, Pages 679 – 686 Shin-Tae Bae, Dong Kyun Yim, Kug Sun Hong, Jin-Soo Park, Hyunho Shin, Hyun Suk JungPublished Online: Nov 5 2008 3:15PM DOI: 10.1111/j.1744-7402.2008.02319.x…
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