582 Data Mining and Machine Learning in Molecular Sciences II

Wednesday, November 11, 2015: 3:15 PM - 5:45 PM
255A (Salt Palace Convention Center)

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.

Computational Molecular Science and Engineering Forum
Andrew L. Ferguson Email: alf@illinois.edu
Johannes Hachmann Email: hachmann@buffalo.edu

4:00 PM

4:30 PM

4:45 PM
(582e) Application of a Genetic Algorithm to Screen Metal-Organic Frameworks: Pre-Combustion CO2/H2 Separation
Yongchul G. Chung, Diego Gomez-Gualdron, Peng Li, Nicolaas Vermeulen, Pravas Deria, Fraser Stoddart, Joseph T. Hupp, Omar K. Farha and Randall Q. Snurr

5:00 PM
(582f) Computational Screening of MOFs with Open Metal Sites
Emmanuel Haldoupis, Konstantinos D. Vogiatzis, Laura Gagliardi and J. Ilja Siepmann

5:30 PM
(582h) Deconstructing Detailed Reaction Mechanisms to Identify Species and Mine for Data
Fariba Seyedzadeh Khanshan, Pierre L. Bhoorasingh, Belinda L. Slakman, Elliot H. Nash and Richard H. West