{"id":206,"date":"2018-12-18T20:13:15","date_gmt":"2018-12-18T20:13:15","guid":{"rendered":"http:\/\/blogs.oregonstate.edu\/cbee\/?p=206"},"modified":"2019-01-23T20:14:21","modified_gmt":"2019-01-23T20:14:21","slug":"unlocking-the-energy-saving-potential-of-molecular-cages","status":"publish","type":"post","link":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/2018\/12\/18\/unlocking-the-energy-saving-potential-of-molecular-cages\/","title":{"rendered":"Unlocking the energy-saving potential of molecular cages"},"content":{"rendered":"
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Nanosized cages may play a big role in reducing energy consumption in science and industry, and machine-learning research at Oregon State University aims to accelerate the deployment of these remarkable molecules.<\/p>\n

The\u00a0porous organic cage molecules<\/a>\u00a0being studied at OSU are able to selectively capture gas molecules, potentially enabling huge energy savings in the myriad gas separations conducted in the chemical sector.<\/p>\n

\u201cThese porous molecular solids are like sponges that soak up gases discriminately,\u201d said\u00a0Cory Simon<\/a>, assistant professor of chemical engineering and corresponding author of a study published in\u00a0ACS Central Science<\/a>.<\/p>\n

Together, the separation and purification of chemical mixtures is responsible for more than 10 percent of the world\u2019s energy consumption.<\/p>\n

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\"Molecular<\/div>\n<\/div>\n<\/div>\n

Porous cage molecules have nanosized cavities intrinsic to their structure, and gas molecules are attracted to and trapped within these cavities via adsorption.<\/p>\n

\u201cBut each cage adsorbs certain gases more readily than others, and this property potentially makes the cages useful for separating gas mixtures more energy-efficiently,\u201d Simon said.<\/p>\n

However, there are thousands of these cage molecules that could be synthesized \u2013 to make even one of them and test its properties takes months in the lab \u2013 and hundreds of different chemical separations are required in industry; hence the need for a computational approach to sort through the possibilities and find the best molecule for the job at hand.<\/p>\n

Simon exploited the idea that the shape of any given cavity is responsible for which gas molecules it most readily attracts.<\/p>\n

Simon and students Arni Sturluson, Melanie Huynh and Arthur York employed an \u201cunsupervised\u201d machine-learning method to categorize and group together cage molecules based on their cavity shapes and, thus, adsorption properties.<\/p>\n

Unsupervised means the computer did the learning about shape\/property relationships on its own; it wasn\u2019t given any labels to instruct it.<\/p>\n

\u201cJust show the data to the algorithm, and it automatically finds patterns \u2013 \u00a0structure \u2013 in the data,\u201d Simon said.<\/p>\n

The researchers used a training dataset of 74 experimentally synthesized porous organic cage molecules that were each computationally scanned, resulting in a 3D \u201cporosity\u201d image of each similar to an image generated by a CT scan.<\/p>\n

\u201cOn the basis of these 3D images, we took inspiration from a facial recognition algorithm, eigenfaces, to group together cages with similarly shaped cavities,\u201d he said. \u201cUsing the singular value decomposition, we encoded the 3D images of the cages into lower-dimensional vectors.\u201d<\/p>\n

Simon explains the process using the analogy of people\u2019s faces.<\/p>\n

\u201cImagine you were forced to map everyone\u2019s face onto a point in a two-dimensional scatter plot while preserving as much information as you can about the faces,\u201d he said. \u201cSo each face is described by just two numbers, and similar-looking faces are grouped close by in the scatter plot. Essentially, the singular value decomposition performed this encoding, but for porous cage molecules.\u201d<\/p>\n

The research demonstrated that the learned encoding captures the salient features of the cavities of porous cages and can predict properties of the cages that relate to cavity shape.<\/p>\n

\u201cOur methods could be applied to learn latent representations of cavities within other classes of porous materials and of shapes of molecules in general,\u201d Simon said.<\/p>\n

\u2014\u00a0Steve Lundeberg<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Nanosized cages may play a big role in reducing energy consumption in science and industry, and machine-learning research at Oregon State University aims to accelerate the deployment of these remarkable molecules. The\u00a0porous organic cage molecules\u00a0being studied at OSU are able to selectively capture gas molecules, potentially enabling huge energy savings in the myriad gas separations … <\/p>\n

Continue reading “Unlocking the energy-saving potential of molecular cages”<\/span><\/a><\/p>\n","protected":false},"author":8555,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-206","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/posts\/206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/users\/8555"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/comments?post=206"}],"version-history":[{"count":1,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/posts\/206\/revisions"}],"predecessor-version":[{"id":207,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/posts\/206\/revisions\/207"}],"wp:attachment":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/media?parent=206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/categories?post=206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/cbee\/wp-json\/wp\/v2\/tags?post=206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}