{"id":651,"date":"2018-05-14T09:46:16","date_gmt":"2018-05-14T13:46:16","guid":{"rendered":"https:\/\/events.mcs.cmu.edu\/mlse\/?page_id=651"},"modified":"2018-06-04T14:19:04","modified_gmt":"2018-06-04T18:19:04","slug":"chemistry-program","status":"publish","type":"page","link":"https:\/\/events.mcs.cmu.edu\/mlse\/chemistry-program\/","title":{"rendered":"Chemistry Program"},"content":{"rendered":"<div id=\"pl-651\" class=\"panel-layout\">\n<div id=\"pg-651-0\" class=\"panel-grid panel-no-style\">\n<div id=\"pgc-651-0-0\" class=\"panel-grid-cell\">\n<div id=\"panel-651-0-0-0\" class=\"so-panel widget widget_sow-editor panel-first-child\">\n<div class=\"so-widget-sow-editor so-widget-sow-editor-base\">\n<div class=\"siteorigin-widget-tinymce textwidget\">\n<p>June 7th<\/p>\n<ul>\n<li>Session Topic<\/li>\n<li>9:00-9:30<\/li>\n<li>9:30-10:00<\/li>\n<li>10:00-10:30<\/li>\n<li>break<\/li>\n<li>11:00-11:30<\/li>\n<li>11:30-12:00<\/li>\n<li>12:00-12:30<\/li>\n<li>12:30-1:30 &#8211; lunch\/posters<\/li>\n<li>1:30-2:30 &#8211; plenary<\/li>\n<li>Session Topic<\/li>\n<li>2:30-3:00<\/li>\n<li>3:00-3:30<\/li>\n<li>3:30-4:00<\/li>\n<li>break<\/li>\n<li>4:30-5:00<\/li>\n<li>5:00-5:30<\/li>\n<li>5:30-6:00<\/li>\n<\/ul>\n<h2>June 8th<\/h2>\n<ul>\n<li>Session Topic &#8211; Predicting Molecular Properties<\/li>\n<li>9:00-9:30 &#8211; Kieron Burke (UC Irvine) \u2013 \u201cUsing Machine Learning to Create New Density Functional Approximations\u201d<\/li>\n<li>9:30-10:00 &#8211; Johannes Hachmann ( Buffalo) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-hachmann.pdf\">Advancing Molecular Property Predictions and Design with Machine Learning<\/a>\u201d<\/li>\n<li>10:00-10:30 &#8211; Noa Marom (Carnegie Mellon) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-NMarom.docx\">Molecular Crystal Structure Prediction with GAtor and Genarris<\/a>\u201d<\/li>\n<li>break<\/li>\n<li>11:00-11:30 &#8211; Le Song (Georgia Tech) \u2013 \u201cDeep Graph Embedding for Molecular Property Prediction and Optimization\u201d<\/li>\n<li>11:30-12:00 &#8211; Deyu Lu (Brookhaven ) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-ML-XAS-DeyuLu.docx\">Application of Machine Learning in X-ray Absorption Spectroscopy<\/a>\u201d<\/li>\n<li>12:00-12:30 &#8211; Olexandr Isayev (UNC) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/Isayev_ani_2018.docx\">Neural Networks Learning Quantum Chemistry<\/a>\u201d<\/li>\n<li>12:30-1:30 &#8211; lunch\/posters<\/li>\n<li>1:30-2:30 &#8211; plenary<\/li>\n<li>Session Topic &#8211; Molecular Design<\/li>\n<li>2:30-3:00 &#8211; Geoff Hutchison ( Pittsburgh) \u2013 \u201cRapid Discovery of Molecular Materials &#8211; Combining Machine Learning and Evolutionary Algorithms\u201d<\/li>\n<li>3:00-3:30 &#8211; Joshua Schrier (Haverford) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/abstract_MLSE2018.docx\">Data-Driven Approaches to Predicting Reaction Outcomes in Solid State Chemistry<\/a>\u201d<\/li>\n<li>3:30-4:00 &#8211; Dmitry Zubarev (IBM)\u2013 &#8220;Bridging Gaps Between Computational and Experimental Aspects of the Fourth Paradigm&#8221;<\/li>\n<li>break<\/li>\n<li>4:30-4:50 &#8211; Carlos Borca (Georgia Tech) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/2018_MLSE_Abstract_CrystaLattE.docx\">CrystalLattE: Automated Computation of Benchmark-Level Lattice Energies of Molecular Crystals<\/a>\u201d<\/li>\n<li>4:50-5:10 &#8211; David Sheen (NIST)\u00a0\u2013 &#8220;Chemometric Analysis of Hydrocarbon Reference Materials for Certification as Aircraft Fuels&#8221;<\/li>\n<li>5:10-5:30 &#8211; David Yaron (Carnegie Mellon) \u2013 \u201cA Quantum Chemical Layer for Deep learning of Electronic Properties\u201d<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"panel-651-0-0-1\" class=\"so-panel widget widget_sow-editor panel-last-child\">\n<div class=\"so-widget-sow-editor so-widget-sow-editor-base\">\n<div class=\"siteorigin-widget-tinymce textwidget\">\n<h3 style=\"text-align: center\">Posters<\/h3>\n<p>Mojtaba Haghighatlari (Buffalo) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/Mojtaba_Haghighatlari_MLSE.pdf\">Software development and its application for predicting optical properties in molecular sspace: ChemML program suite<\/a>\u201d<\/p>\n<p>Xi Chen (Brown) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE_abstract_xichen.docx\">The application of machine learning in variational transition state theory<\/a>\u201d<\/p>\n<p>Derek Metcalf (MSU) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE_2018_abstract.pdf\">Using Bayesian neural networks to understand uncertainty in model neural network chemistry predictions<\/a>\u201d<\/p>\n<p>Chen Qu (Emory) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-Qu.docx\">Assessing the Gaussian process approach in potential energy surface fitting<\/a>\u201d<\/p>\n<p>Mohammad Atif Afzal (Buffalo) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-Afzal.pdf\">Harnessing virtual high-throughput screening and machine learning for the discovery of novel high-refractive-index polymers<\/a>\u201d<\/p>\n<p>Holden Parks (CMU) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-template-2.docx\">Quantifying uncertainty in first-principles predictions of molecular vibrational frequencies with applications to machine learning<\/a>\u201d<\/p>\n<p>Haichen Li (CMU) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract-atrprl.docx\">Using deep reinformcement learning to guide chemical reactions<\/a>\u201d<\/p>\n<p>Michael Taylor (Pittsburgh) \u2013 \u201cTBD\u201d<\/p>\n<p>Mariya Popova (UNC) \u2013 \u201c<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/ninja-forms\/2\/MLSE-abstract_Mariya-Popova.docx\">De-novo drug design with deep reinforcement learning<\/a>\u201d<\/p>\n<p>Christopher Kotke (CMU) \u2013 \u201cTBD\u201d<\/p>\n<p>Run Li (Florida State) \u2013 \u201cWITHDRAWN- Development of complex v2RDM driven relativistic CASSCF methods\u201d<\/p>\n<p>Timothy Rose (CMU) \u2013 \u201cEvolutionary niching in the GAtor genetic algorithym for molecular crystal structure prediction\u201d<\/p>\n<p>Xingyu Liu (CMU) \u2013 \u201cAccelerate searching for singlet fission materials: Feature selection and model construction for GW+BSE method with SISSO\u201d<\/p>\n<p>Joon-Yong Lee (PNNL)\u00a0\u2013 &#8220;<a href=\"https:\/\/events.mcs.cmu.edu\/mlse\/wp-content\/uploads\/sites\/2\/2018\/06\/ECE-Lee.pdf\">Deep Learning Benchmark Data for de novo Peptide Sequencing<\/a>&#8220;<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>June 7th Session Topic 9:00-9:30 9:30-10:00 10:00-10:30 break 11:00-11:30 11:30-12:00 12:00-12:30 12:30-1:30 &#8211; lunch\/posters 1:30-2:30 &#8211; plenary Session Topic 2:30-3:00 3:00-3:30 3:30-4:00 break 4:30-5:00 5:00-5:30 5:30-6:00 June 8th Session Topic &#8211; Predicting Molecular Properties 9:00-9:30 &#8211; Kieron Burke (UC Irvine) \u2013 \u201cUsing Machine Learning to Create New Density Functional Approximations\u201d [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":5,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-651","page","type-page","status-publish","has-post-thumbnail","hentry"],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/P9mDlk-av","_links":{"self":[{"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/pages\/651","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/comments?post=651"}],"version-history":[{"count":11,"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/pages\/651\/revisions"}],"predecessor-version":[{"id":978,"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/pages\/651\/revisions\/978"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/media\/5"}],"wp:attachment":[{"href":"https:\/\/events.mcs.cmu.edu\/mlse\/wp-json\/wp\/v2\/media?parent=651"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}