142 Data Mining and Machine Learning in Molecular Sciences I

Monday, November 14, 2016: 12:30 PM - 3:00 PM
Yosemite A (Hilton San Francisco Union Square)
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
Information Management and Intelligent Systems (10E)

Andrew L. Ferguson
Email: alf@illinois.edu

Johannes Hachmann
Email: hachmann@buffalo.edu

1:24 PM
(142d) Guiding Experiments Towards New Functional Materials with Informatics
Prasanna V. Balachandran, Dezhen Xue and Turab Lookman

1:36 PM
(142e) Pushing the Frontiers of Atomistic Modeling Towards Predictive Design of Materials
Subramanian Sankaranarayanan, Badri Narayanan and Mathew Cherukara

1:48 PM
(142f) Design of Ternary Transparent Conducting Oxides
Christopher Sutton, Matthias Scheffler and Luca M. Ghiringhelli

2:12 PM
(142h) Machine Learning for Advancing Discovery of Novel Thermoelectric Materials. the Thermoel
Al'ona Furmanchuk, Ankit Agrawal, James Saal, Jeff Doak, Gregory Olson and Alok Choudhary

2:48 PM