{"id":14025,"date":"2025-04-24T23:15:26","date_gmt":"2025-04-24T23:15:26","guid":{"rendered":"https:\/\/scienceweb.clemson.edu\/uacl-new\/?p=14025"},"modified":"2025-11-06T02:06:37","modified_gmt":"2025-11-06T02:06:37","slug":"des-properties-via-machine-learning","status":"publish","type":"post","link":"https:\/\/scienceweb.clemson.edu\/uacl\/des-properties-via-machine-learning\/","title":{"rendered":"DES Properties via Machine Learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"14025\" class=\"elementor elementor-14025\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5aba47e e-flex e-con-boxed mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-parent\" data-id=\"5aba47e\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-f42177f e-con-full e-flex mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-child\" data-id=\"f42177f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b2f4cec mk-enable-fade-animation-none elementor-widget elementor-widget-image\" data-id=\"b2f4cec\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_infinite&quot;:&quot;false&quot;,&quot;mk_ext_is_scrollme&quot;:&quot;false&quot;}\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"290\" height=\"300\" src=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/REU_project_01-290x300.png\" class=\"attachment-medium size-medium wp-image-14100\" alt=\"REU project 01\" srcset=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/REU_project_01-290x300.png 290w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/REU_project_01.png 750w\" sizes=\"(max-width: 290px) 100vw, 290px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0a8fbdd e-con-full e-flex mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-child\" data-id=\"0a8fbdd\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f5f18bc mk-enable-fade-animation-none elementor-widget elementor-widget-text-editor\" data-id=\"f5f18bc\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_infinite&quot;:&quot;false&quot;,&quot;mk_ext_is_scrollme&quot;:&quot;false&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"color: #000000\">We have recently demonstrated the utility of AI (Transformer-Based Model) to predict the formation of multiple, new, and stable DES (n=337) from a general database of natural compounds. We have also expanded these studies to more complex models, leading to predictions of the melting point of DES with an average accuracy of 98%. Building upon these findings, participants in this project will receive training in basic machine learning techniques and in the implementation of <em>molecular fingerprints<\/em> to predict the formation as well as melting point of DES. Unlike text-based representations of chemical structures, molecular fingerprints are derived from molecular graphs, enabling calculations based on global molecular descriptors that preserve the chemical identity of functional groups. For us, the most significant advantage is that these vectors (also known as descriptors) can be used to import properties of interest, such as molecular structure and chemical features,\u00a0directly from PubChem. Participants will be trained to improve our current database (containing almost 1800 examples of DES) and then train their own neural networks.\u00a0Participants gain knowledge related to the architecture of the network, the structure of the databases, and how to test the model\u2019s accuracy to avoid <em>overfitting<\/em> (where the neural net essentially memorizes the training examples but fails to generalize its \u2018understanding\u2019 of the subject matter). We expect participants to try several tweaks to optimize the algorithm&#8217;s performance, following the evolution of the loss parameter, the validation error (expected to decrease), and the training error as a function of the number of epochs. If this is insufficient, we will implement additional (more complex) strategies, as previously described. During the last three weeks of the program, students will be asked to select a target molecule (from a curated list of active pharmaceutical ingredients) and apply their algorithm to predict the components and molar ratios needed to produce at least 25 new DES. Out of those, they will be asked to make at least 10 DES integrating the selected compound and determine their stability and melting points. This exercise will allow participants not only to challenge their model but also to address a critical aspect affecting the clinical use of these DES.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-466ea5f e-flex e-con-boxed mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-parent\" data-id=\"466ea5f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-258e686 mk-enable-fade-animation-none elementor-widget elementor-widget-spacer\" data-id=\"258e686\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_infinite&quot;:&quot;false&quot;,&quot;mk_ext_is_scrollme&quot;:&quot;false&quot;}\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-92471e0 e-flex e-con-boxed mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-parent\" data-id=\"92471e0\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-d76b779 e-con-full e-flex mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-child\" data-id=\"d76b779\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6e09e60 mk-enable-fade-animation-none elementor-widget elementor-widget-image\" data-id=\"6e09e60\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_infinite&quot;:&quot;false&quot;,&quot;mk_ext_is_scrollme&quot;:&quot;false&quot;}\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"1024\" data-src=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-768x1024.jpeg\" class=\"attachment-large size-large wp-image-13858 lazyload\" alt=\"Portrait of Dr. Garcia\" data-srcset=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-768x1024.jpeg 768w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-225x300.jpeg 225w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-1152x1536.jpeg 1152w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-1536x2048.jpeg 1536w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/IMG_6639-scaled.jpeg 1920w\" data-sizes=\"(max-width: 768px) 100vw, 768px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 768px; --smush-placeholder-aspect-ratio: 768\/1024;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-af29690 e-con-full e-flex mk-enable-fade-animation-none mk-sticky-column-false mk-sticky-column-disable-tablet e-con e-child\" data-id=\"af29690\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_sticky&quot;:&quot;false&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1aac173 mk-enable-fade-animation-none elementor-widget elementor-widget-text-editor\" data-id=\"1aac173\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;mk-animation-effect&quot;:&quot;none&quot;,&quot;mk_ext_is_infinite&quot;:&quot;false&quot;,&quot;mk_ext_is_scrollme&quot;:&quot;false&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"color: #000000\"><strong>Dr. Carlos D. Garcia<br \/><\/strong><\/span><strong style=\"background-color: transparent;color: #000000;font-family: inherit;font-style: inherit;letter-spacing: 0em\">Clemson University<\/strong><\/p><p><span style=\"color: #000000\"><a style=\"color: #000000\" href=\"https:\/\/scienceweb.clemson.edu\/uacl\/\"><img decoding=\"async\" class=\"size-full wp-image-14115 lazyload\" title=\"icons8 website\" data-src=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/icons8-website.gif\" alt=\"icons8 website\" width=\"50\" height=\"50\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 50px; --smush-placeholder-aspect-ratio: 50\/50;\" \/><\/a> <a style=\"color: #000000\" href=\"https:\/\/scholar.google.com\/citations?user=8GhoYkUAAAAJ&amp;hl=en&amp;oi=ao\"><img decoding=\"async\" class=\"size-full wp-image-14116 lazyload\" title=\"icons8 google scholar 50\" data-src=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/icons8-google-scholar-50.png\" alt=\"icons8 google scholar 50\" width=\"50\" height=\"50\" data-srcset=\"https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/icons8-google-scholar-50.png 50w, https:\/\/scienceweb.clemson.edu\/uacl\/wp-content\/uploads\/sites\/46\/2025\/04\/elementor\/thumbs\/icons8-google-scholar-50-r53orhp59nh1i1yl0nelevkoz1i2lwo3xev7el2dy6.png 35w\" data-sizes=\"(max-width: 50px) 100vw, 50px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 50px; --smush-placeholder-aspect-ratio: 50\/50;\" \/><\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>We have recently demonstrated the utility of AI (Transformer-Based Model) to predict the formation of multiple, new, and stable DES (n=337) from a general database of natural compounds. We have also expanded these studies to more complex models, leading to predictions of the melting point of DES with an average accuracy of 98%. Building upon these findings, participants in this project will receive training in basic machine learning techniques and in the implementation of molecular fingerprints to predict the formation as well as melting point of DES. Unlike text-based representations of chemical structures, molecular fingerprints are derived from molecular graphs, [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":14100,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[158],"tags":[],"class_list":["post-14025","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-reu_03_projects"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DES Properties via Machine Learning - Microanalytical Chemistry Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/scienceweb.clemson.edu\/uacl\/des-properties-via-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DES Properties via Machine Learning - Microanalytical Chemistry Lab\" \/>\n<meta property=\"og:description\" content=\"We have recently demonstrated the utility of AI (Transformer-Based Model) to predict the formation of multiple, new, and stable DES (n=337) from a general database of natural compounds. We have also expanded these studies to more complex models, leading to predictions of the melting point of DES with an average accuracy of 98%. Building upon these findings, participants in this project will receive training in basic machine learning techniques and in the implementation of molecular fingerprints to predict the formation as well as melting point of DES. 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We have also expanded these studies to more complex models, leading to predictions of the melting point of DES with an average accuracy of 98%. Building upon these findings, participants in this project will receive training in basic machine learning techniques and in the implementation of molecular fingerprints to predict the formation as well as melting point of DES. 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