777 Data Mining and Machine Learning in Molecular Sciences II

Friday, November 18, 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

1:00 PM

1:48 PM
(777f) An Automated Approach for Developing Graph-Theoretical Cluster Expansions of the Total Energy of Adsorbed Layers
Emanuele Vignola, Stephan N. Steinmann, Michail Stamatakis and Phillippe Sautet

2:12 PM
(777h) Local Pattern Discovery for Uncovering Structure-Property Relationships of Materials
Bryan R Goldsmith, Mario Boley, Luca M. Ghiringhelli and Matthias Scheffler

2:24 PM
(777i) Alloy Catalyst Discovery Using Computational Alchemy
Karthikeyan Saravanan, O. Anatole von Lilienfeld and John A. Keith

2:36 PM