1.0Microanalytical Chemistry Labhttps://scienceweb.clemson.edu/uaclCDGhttps://scienceweb.clemson.edu/uacl/author/carlos/DES Properties via Machine Learning - Microanalytical Chemistry Labrich600338<blockquote class="wp-embedded-content" data-secret="nnfymswOcx"><a href="https://scienceweb.clemson.edu/uacl/des-properties-via-machine-learning/">DES Properties via Machine Learning</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://scienceweb.clemson.edu/uacl/des-properties-via-machine-learning/embed/#?secret=nnfymswOcx" width="600" height="338" title="“DES Properties via Machine Learning” — Microanalytical Chemistry Lab" data-secret="nnfymswOcx" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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https://scienceweb.clemson.edu/uacl/wp-content/uploads/sites/46/2025/04/REU_project_01.png750775We 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, […]