NSF Big Data Spoke on Advancing a Data-Driven Discovery and Rational Design Paradigm in Chemistry
PI: Dr. Johannes Hachmann (UB); co-PIs: Dr. Geoffrey Hutchison (University of Pittsburgh), Dr. Marcus Hanwell (Kitware, Inc.)
NSF grant no. IIS-1761990
The NSF Big Data Spoke on Advancing a Data-Driven Discovery and Rational Design Paradigm in Chemistry (funded under NSF grant no. IIS-1761990) sets out to promote the role of modern data science in the chemical domain and to foster and coalesce a community of stakeholders. It aims to create a community-driven roadmap as well as facilitate concrete solutions that go beyond the scope of the disjointed efforts of its individual stakeholders. It thus seeks to implement some of the key findings of the 2017 NSF Division of Chemistry workshop on Framing the Role of Big Data and Modern Data Science in Chemistry. The NSF Big Data Innovation Hubs and Spokes ecosystem is an ideal framework to realizing this vision and accelerating progress in this high-priority area of research, and this Spoke is part of the Northeast Hub.
The four signature initiatives of this Spoke are:
- Tools: Planning, coordination, integration, and consolidation of community-developed software tools for big data research in chemistry as well as establishing guidelines, best practices, and standards;
- Infrastructure: Providing access to a shared hardware infrastructure for community data sets, on-site data mining capacity, and the exploration of domain specific method and hardware issues;
- Meetings: Organizing regular workshops for community building, to connect solution seekers with solution providers, and to address questions ranging from strategic to technical;
- Education: Creating and disseminating community-developed teaching materials, including course, program, and curricular recommendations for education and workforce development that reflect a data-centric approach in chemical research.
Here is a list of the Spoke’s Core Stakeholders:
| Name | Affiliation | Role |
|---|---|---|
| Clementi, Cecilia | Humboldt University Berlin (Germany), Rice University | |
| Crawford, Daniel | Virginia Tech, MolSSI | MolSSI Liaison |
| Cummings, Peter | Vanderbilt University | |
| Glotzer, Sharon | University of Michigan | |
| Grover, Martha | Georgia Tech | |
| Hachmann, Johannes | University at Buffalo – SUNY | PI |
| Hanwell, Marcus | Brookhaven National Lab | co-PI, Technical Lead |
| Harrison, Robert | Stony Brook University | IACS Liaison |
| Hernandez, Rigoberto | Johns Hopkins University | |
| Hutchison, Geoffrey | University of Pittsburgh | co-PI |
| Isayev, Olexandr | Carnegie Mellon University | |
| Kulik, Heather | MIT | |
| Marom, Noa | Carnegie Mellon University | |
| McEwen, Leah Ray | Cornell University | |
| Meredig, Bryce | Citrine | |
| Moore, Jonathan | Dow Chemical | CACHE Liaison |
| Persson, Kristin | Lawrence Berkeley National Lab | |
| Pfaendtner, Jim | University of Washington | CoMSEF Liaison |
| Roitberg, Adrian | University of Florida | |
| Saxe, Paul | Virginia Tech, MolSSI | MolSSI Liaison |
| Schrier, Joshua | Fordham University | |
| Sherrill, David | Georgia Tech | |
| Tuckerman, Mark | New York University | |
| von Lilienfeld, Anatole | University of Vienna (Austria) | |
| Warren, James | NIST | NIST Liaison |
| West, Richard | Northeastern University | |
| White, Andrew | University of Rochester | |
| Williams, Antony | EPA | |
| Wolverton, Chris | Northwestern University | |
| Yaron, David | Carnegie Mellon University |
Disclaimer
This material is based upon work supported by the National Science Foundation under grant no. IIS-1761990. Any opinions, findings, conclusions, and/or recommendations expressed in this material are those of the Big Data Spoke participants and do not necessarily reflect the views of the National Science Foundation.
(Last update: 2021-07-26)