Data Mining and Machine Learning in Molecular Sciences I Conference: AIChE Annual MeetingYear: 2016Proceeding: 2016 AIChE Annual MeetingGroup: Computational Molecular Science and Engineering Forum Type: Oral Room: Yosemite A Location: Hilton San Francisco Union Square Time: Monday, November 14, 2016 - 12:30pm-3:00pm Chair(s):Ferguson, A. L., University of Illinois at Urbana-Champaign Co-chair(s):Hachmann, J., University at Buffalo, SUNY Computational approaches to correlate, analyze, and understand large and complex data sets are playing increasingly important roles in the physical, chemical, and life sciences. This session solicits submissions pertaining to methodological advances and applications of data mining and machine learning methods, with particular emphasis on data-driven modeling and property prediction, statistical inference, big data, and informatics. Topics of interest include: algorithm development, inverse engineering, chemical property prediction, genomics/proteomics/metabolomics, (virtual) high-throughput screening, rational design, accelerated simulation, biomolecular folding, reaction networks, and quantum chemistry. Papers: 12:30 PM(142a) Mathematics for Data-Driven Modeling - the Science of Crystal BallsIoannis G. Kevrekidis 1:00 PM(142b) Decoding Common Features of Protein-Nanoparticle InteractionsQing ShaoCarol K. Hall 1:12 PM(142c) Design of Optimal Experimental Probes for Protein Dynamics Using Machine Learning and Variational Approach to Modeling Conformational KineticsBalaji SelvamShriyaa MittalChuankai ZhaoDiwakar Shukla 1:24 PM(142d) Guiding Experiments Towards New Functional Materials with InformaticsPrasanna V. BalachandranDezhen XueTurab Lookman 1:36 PM(142e) Pushing the Frontiers of Atomistic Modeling Towards Predictive Design of MaterialsSubramanian SankaranarayananBadri NarayananMathew Cherukara 1:48 PM(142f) Design of Ternary Transparent Conducting OxidesChristopher SuttonMatthias SchefflerLuca M. Ghiringhelli 2:00 PM(142g) Development of Empirical Charge Transfer Interatomic Potential for Tantalum Oxide Nanostructures from First Principle CalculationsKiran SasikumarBadri NarayananSubramanian K.R.S. Sankaranarayanan 2:12 PM(142h) Machine Learning for Advancing Discovery of Novel Thermoelectric Materials. the ThermoelAl'ona FurmanchukAnkit AgrawalJames SaalJeff DoakGregory OlsonAlok Choudhary 2:24 PM(142i) Identifying Descriptors for Dielectric Breakdown Strength Using Genetic ProgrammingFenglin YuanTim Mueller 2:36 PM(142j) Machine Learning with Structural Fingerprints of Local Particle EnvironmentsMatthew SpellingsSharon C. Glotzer 2:48 PM(142k) Using Semi-Supervised Machine Learning to Map the Phase Diagrams of Open Materials Data SetsJason Hattrick-SimpersJonathan Kenneth BunnJianjun Hu Topics: Thermodynamics Checkout Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings. Checkout Do you already own this? Log In for instructions on accessing this content. Pricing Individuals AIChE Members $150.00 AIChE Graduate Student Members Free AIChE Undergraduate Student Members Free Non-Members $225.00