A quick reminder that today is the deadline for the submission to the AIChE CoMSEF session on "Data Mining and Machine Learning in Molecular Sciences" at the 2016 AIChE Annual Meeting in San Francisco.

Best regards,

Johannes Hachmann

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Dear Colleagues,

We are writing today to let you know that we will again be running the CoMSEF technical session "Data Mining and Machine Learning in Molecular Sciences" at the 2016 AIChE Annual Meeting in San Francisco (Nov 13-18).

Last year's inaugural edition proved to be exceedingly popular and well-attended, indicative of a critical groundswell of excitement and interest within the ChemE community for data-driven methods and applications in the physical, chemical, materials, and life sciences. We are also delighted to announce that this year's session will be anchored by two invited talks from Yannis Kevrekidis (Princeton) and Kristin Persson (Lawrence Berkeley National Lab).

We are currently soliciting abstracts for contributed talks, and if you or your students are interested in presenting in this session we would be excited to receive your submission through the online application portal. The scope of the session is intentionally broad, concerning the generic applications of data mining and machine learning for property prediction, molecular understanding, and rational design. Details of the session scope and instructions for abstract submission are provided below. The submission deadline is Monday, May 9.

We look forward to seeing you in San Francisco!

Kind Regards,

Andrew Ferguson (University of Illinois, alf[]illinois.edu)

Johannes Hachmann (University at Buffalo, hachmann[]buffalo.edu)

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Data Mining and Machine Learning in Molecular Sciences

https://aiche.confex.com/aiche/2016/webprogrampreliminary/Session32684.html

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.

1. Go to https://aiche.confex.com/aiche/2016/cfp.cgi
2. Click on the blue drop down for "Computational Molecular Science and Engineering Forum", and click "Begin a Submission"
3. Select "21004 Data Mining and Machine Learning in Molecular Sciences and then click Save and Continue".

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Dr. Johannes Hachmann

Assistant Professor

University at Buffalo, The State University of New York

Department of Chemical and Biological Engineering (CBE)

New York State Center of Excellence in Materials Informatics (CMI)

Computational and Data-Enabled Science and Engineering Program (CDSE)

612 Furnas Hall

Buffalo, NY 14260

http://www.cbe.buffalo.edu/hachmann <http://www.cbe.buffalo.edu/hachmann>

http://hachmannlab.cbe.buffalo.edu <http://hachmannlab.cbe.buffalo.edu/>

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