{"id":1014,"date":"2019-09-18T17:17:04","date_gmt":"2019-09-18T22:17:04","guid":{"rendered":"http:\/\/www.lab-paris.com\/?p=1014"},"modified":"2019-09-18T17:17:04","modified_gmt":"2019-09-18T22:17:04","slug":"irradiation-vs-vitrification-induced-damage-in-materials-2-2-2-2","status":"publish","type":"post","link":"https:\/\/www.lab-paris.com\/?p=1014","title":{"rendered":"Predicting optimal glass compositions"},"content":{"rendered":"\n<p><\/p>\n\n\n<p>For glass scientists, the periodic table is their oyster\u2014virtually all elements turn into a glass if quenched fast enough. Yet with so many options, finding \u201cpearls\u201d (optimal glass compositions) among the essentially limitless possibilities is extremely difficult when relying on trial-and-error experimental approaches.<\/p>\n<p>Numerous recent studies use machine learning to predict glass composition. What these studies collectively teach us, and what they offer to future research, is the subject of a new open-access\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.nocx.2019.100036\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">review paper<\/a>\u00a0by researchers at the University of California, Los Angeles.<\/p>\n<p><a href=\"https:\/\/ceramics.org\/ceramic-tech-today\/modeling-simulation\/predicting-optimal-glass-compositions-a-review-of-machine-learning-for-glass-science-and-engineering\">Read the rest of the story on Ceramic Tech Today\u00a0<\/a><\/p>\n<p>\u00a0<\/p>","protected":false},"excerpt":{"rendered":"<p>For glass scientists, the periodic table is their oyster\u2014virtually all elements turn into a glass if quenched fast enough. Yet [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/posts\/1014"}],"collection":[{"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1014"}],"version-history":[{"count":1,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions"}],"predecessor-version":[{"id":1015,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=\/wp\/v2\/posts\/1014\/revisions\/1015"}],"wp:attachment":[{"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1014"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1014"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.lab-paris.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1014"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}