{"id":46528,"date":"2023-05-15T14:00:53","date_gmt":"2023-05-15T12:00:53","guid":{"rendered":"https:\/\/www.h-its.org\/de\/event\/kolloquium-olexandr-isayev-accelerating-design-of-organic-materials-with-machine-learning-and-ai\/"},"modified":"2023-06-05T11:34:39","modified_gmt":"2023-06-05T09:34:39","slug":"kolloquium-olexandr-isayev","status":"publish","type":"tribe_events","link":"https:\/\/www.h-its.org\/de\/event\/kolloquium-olexandr-isayev\/","title":{"rendered":"Kolloquium Olexandr Isayev: Accelerating Design of Organic Materials with Machine Learning and AI"},"content":{"rendered":"

 <\/p>\n

By Olexandr Isayev<\/a>, Carnegie Mellon University, Department of Chemistry, Pittsburgh, Pennsylvania, USA<\/p>\n

Deep learning is revolutionizing many areas of science and technology, particularly in natural language processing, speech recognition, and computer vision. In this talk, we will provide an overview of the latest developments of machine learning and AI methods and their application to the problem of molecular discovery and rational design at Isayev\u2019s Lab at CMU. We identify several areas where existing methods have the potential to accelerate materials research and disrupt more traditional approaches. First, we will present a deep learning model that approximates the solution of the Schrodinger equation. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity. The AIMNet-NSE model allows us to bypass QM calculations fully and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors and learned atomic representations could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. Second, we proposed a novel ML-guided materials discovery platform that combines synergistic innovations in automated flow synthesis and automated machine learning (AutoML) method development. A software-controlled, continuous polymer synthesis platform enables rapid iterative experimental\u2013computational cycles that result in the synthesis of hundreds of unique copolymer compositions within a multi-variable compositional space. The non-intuitive design criteria identified by ML, accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19F MRI agents and led to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.<\/p>\n

 <\/p>\n

Short CV:<\/strong><\/p>\n

Olexandr Isayev is an Associate Professor in the Department of Chemistry at Carnegie Mellon University. In 2008, Olexandr received his Ph.D. in computational chemistry. He was Postdoctoral Research Fellow at the Case Western Reserve University and a scientist at the government research lab. During 2016-2019 he was a faculty at UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill. Olexandr received the \u201cEmerging Technology Award\u201d from the American Chemical Society (ACS) and the GPU computing award from NVIDIA. The research in his lab focuses on connecting artificial intelligence (AI) with chemical sciences.<\/p>\n

 <\/p>\n

REGISTRATION:<\/strong><\/p>\n

The talk will be hybrid.<\/article>\n
If you would like to participate in person, please register in advance at benedicta.frech@h-its.org<\/a>.<\/article>\n
If you would like to participate online, please register in advance here: https:\/\/kta-email.zoom.us\/meeting\/register\/tJMudeGqqTgqE9AKlyirJF9Cv0xXYH0EGSTV.<\/a><\/article>\n
After registering, you will receive a confirmation email containing information about joining the meeting.<\/article>\n
<\/article>\n
<\/article>\n
In case you are not able to attend, you can watch the talk afterwards on the HITS YouTube channel:\u00a0https:\/\/www.youtube.com\/user\/TheHITSters<\/a>.<\/article>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

  By Olexandr Isayev, Carnegie Mellon University, Department of Chemistry, Pittsburgh, Pennsylvania, USA Deep learning is revolutionizing many areas of science …<\/p>\n","protected":false},"author":42,"featured_media":46530,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"hits-research-group":[1410,1319],"tribe_events_cat":[1345,1408],"class_list":["post-46528","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","hits-research-group-computational-carbon-chemistry","hits-research-group-hits","tribe_events_cat-oeffentliche-veranstaltungen","tribe_events_cat-kolloquien","cat_oeffentliche-veranstaltungen","cat_kolloquien"],"acf":[],"yoast_head":"\nKolloquium Olexandr Isayev: Accelerating Design of Organic Materials with Machine Learning and AI - HITS gGmbH<\/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:\/\/www.h-its.org\/de\/event\/kolloquium-olexandr-isayev\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kolloquium Olexandr Isayev: Accelerating Design of Organic Materials with Machine Learning and AI - 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