MATDAT18 is a hackathon funded by NSF. This hackathon is to bring together
materials and data scientists with the goal of addressing challenging
problems in materials genomics. Invitees will be fully funded for their
Time and Place
May 15-17, 2018
NSF Headquarters, Alexandria, VA
Andrew Ferguson, Materials Science and Engineering, University of Illinois
Tim Mueller, Materials Science and Engineering, Johns Hopkins University
Sanguthevar Rajasekaran, Computer Science & Engineering, University of
Brian Reich, Department of Statistics, North Carolina State University
Primary Contact: [log in to unmask]
MATDAT18 Webpage: https://matdat18.wordpress.ncsu.edu/
Increases in computing power and advances in high-throughput
instrumentation has led to the generation of computational and experimental
materials science data sets of unprecedented size. Researchers are
increasingly turning to data science tools to analyze these data to extract
understanding, and perform high-throughput screening and data-driven
design. An impediment to success is that materials experts may not be
experts in data science, and data scientists typically lack the
domain-specific expertise in materials engineering. It is the goal of this
3-day “hackathon” to partner materials and data scientists within
interdisciplinary teams to spark collaborative research partnerships.
Materials researchers will develop fluency in statistical and machine
learning techniques, and data scientists will be exposed to data-centric
problems in materials engineering. Full financial support is available to
Step 1 – Solicitation of data-centric projects from materials researchers.
Deadline: January 15, 2018
Materials scientists interested in proposing a project for the hackathon
should complete the attached application form and submit via email to Brian
Reich ([log in to unmask]). A (non-exhaustive) list of sample projects is
provided below as examples of possible topics.
Step 2 – Release and advertisement of materials projects to data scientists.
Deadline: Februay 15, 2018
The organizers will sort the submitted projects, host them online, and
advertise to the data science community through conferences, publications,
and workshops. Data science applicants should complete the online
application through the webpage at: https://matdat18.wordpress.ncsu.edu/.
The organizers will pair teams, perform remote introductions, and support
preliminary preparation and goal setting in advances of the hackathon.
Step 3 – Solicitation of intention from data scientists to work on specific
Deadline: March 15, 2018
Data scientists interested in working on any specific materials project(s)
identified by the organizers (in Step 2 above) should complete the attached
application form and submit via email to Brian Reich ([log in to unmask]).
Step 4 – Announcement of identified teams
Deadline: April 1, 2018
The organizers will identify teams based on the inputs collected from the
materials and data scientists (in Steps 1 and 3 above). Each teach team
will consist of (≤) 2 materials experts and (≤) 2 data scientists.
Support and Sponsors
Full financial support is available for participant travel, accommodation,
and all meals.
Supported by the National Science Foundation under Grant No. DMR-1748198.
Jointly supported by the Division of Materials Research (DMR), Division of
Information and Intelligent Systems (IIS), and Division of Mathematical
• Data-driven design of experiment and simulation
• Inverse data-driven materials design
• Machine learning of quantitative structure activity relationship (QSAR)
• Predicting the properties of materials
• Identifying descriptors of materials performance
• Identifying patterns in experimental data (e.g. micrographs).
• Dimensionality reduction, exploration, and exploitation of
high-dimensional data sets
• Discovery and design of sequence-defined cell-penetrating peptides and
• Composition formulation of designer alloys
• Optimal design of substrate patterning for polymeric assembly
• Design of interaction potentials for self-assembling colloidal crystals
• Accelerated discovery of organic semiconductor materials
• Enhanced sampling in molecular simulation
• Materials discovery in large-scale databases
• Bayesian data analysis
• Creation of databases
• Data integration
• Data reduction techniques
• Feature selection
• High Performance techniques
• Machine learning
• Out-of-core algorithms
• Spatial statistics
• Text mining
• Uncertainty quantification
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