Dear Colleagues,

We are writing today to let you know that we will again be running our CoMSEF session on "Data Mining and Machine Learning in Molecular Sciences" at the 2017 AIChE Annual Meeting in Minneapolis, MN (Oct 29 - Nov 3). This year, the session is co-sponsored by Data and Information Systems (10E).

In the previous two years, this session has 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 an invited talk from Prof. David Sholl (Georgia Tech).

We are currently soliciting abstracts for contributed talks, and if you or your students/postdocs 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 and instructions for abstract submission are provided below. The submission deadline is Monday, April 17, i.e., it is rapidly coming up.

We look forward to seeing you in Minneapolis!

Kind Regards,

Johannes Hachmann (University at Buffalo - SUNY)

Andrew Ferguson (University of Illinois Urbana-Champaign)

Diwakar Shukla (University of Illinois Urbana-Champaign)

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Data Mining and Machine Learning in Molecular Sciences

https://aiche.confex.com/aiche/2017/webprogrampreliminary/Session35757.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/2017/webprogrampreliminary/Session35757.html.
2. Click on the orange "Submit an Abstract to this Session" button at the bottom of the page.

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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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