272 Data Mining and Machine Learning in Molecular Sciences I


Description:
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.

Sponsor:
Computational Molecular Science and Engineering Forum
Co-Sponsor(s):
Data and Information Systems (10E), Computing Systems and Technology Division (10)


Chair:
Andrew Ferguson
Assistant Professor
University of Illinois at Urbana-Champaign
Materials Science and Engineering
Urbana, IL
Email: alf@illinois.edu

Co-Chair:
Johannes Hachmann
Assistant Professor
University at Buffalo, SUNY
Computational and Data-Enabled Science and Engineering Graduate Program
Buffalo, NY
Phone Number: 7166451524
Email: hachmann@buffalo.edu


Papers:
272a
Nonlinear Manifold Learning of Nucleosome Dynamics from Molecular Simulation
Ashley Guo, University of Chicago; Joshua Lequieu, UC Santa Barbara; Juan de Pablo, University of Chicago
272b
Machine Learning Predicts Functional Classes of Family 7 Glycoside Hydrolases with High Accuracy
Japheth Gado, University of Kentucky; Anna Borisova, Swedish University of Agricultural Sciences; Jerry Ståhlberg, Swedish University of Agricultural Sciences; Christina M. Payne, National Renewable Energy Laboratory
272c
Implementation and Automation of a Hierarchical Graph Based Approach for Extracting Coarse-Grain Mapping Operators
Maghesree Chakraborty, University of Rochester; Andrew White, University of Rochester
272d
Machine Learning Algorithms for High-Throughtput Chemical Sensing Using Liquid-Crystals
Yankai Cao, University of Wisconsin-Madison; Huaizhe Yu, University of Wisconsin-Madison; Nicholas L. Abbott, University of Wisconsin-Madison; Victor M. Zavala, University of Wisconsin-Madison
272e
Materials Informatics for Process Optimization: Case Studies Using P3HT and PP Composites
Michael McBride, Georgia Institute of Technology; Nils Persson, Georgia Institute of Technology; Elsa Reichmanis, Georgia Institute of Technology; Martha A. Grover, Georgia Institute of Technology
272f
Learning Many-Body Molecular Interactions from Machine Learning
Francesco Paesani, University of California San Diego; Thuong Nguyen, University of California San Diego; Andreas W. Götz, San Diego Supercomputer Center
272g
Combining Machine Learning and Evolutionary Computing for Accelerating Materials Design
Tarak Patra, Argonne National Laboratory; David S. Simmons, University of Akron; Subramanian Sankaranarayanan, Argonne National Laboratory; Badri Narayanan, Argonne National Lab
272h
Predicting Colloidal Crystals from Shapes Via Inverse Design and Machine Learning
Yina Geng, University of Michigan; Greg van Anders, University of Michigan; Sharon C. Glotzer, University of Michigan
272i
Autonomous Crystal Structure Characterization with Neighborhood Graph Analysis
Wesley F. Reinhart, Princeton University; Athanassios Z. Panagiotopoulos, Princeton University
272j
The Physical Analytics Pipeline - a Bayesian Optimization of the Hybrid Organic-Inorganic Perovskite Compositional Space
Henry C. Herbol, Cornell University; Matthias Poloczek, University of Arizona; Paulette Clancy, Cornell University
Abstract Submissions Closed
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