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The National Science Foundation (NSF) 2018 Materials and Data Science Hackathon (MATDAT18) took place at the Residence Inn Alexandria Old Town/Duke Street, Alexandria, VA over the period May 30–June 1, 2018. This three-day collaborative “hackathon” or “datathon” brought together teams of materials scientists and data scientists to collaboratively engage materials science problems using data science tools. The materials scientists brought a diversity of problems ranging from inorganic material bandgap prediction to acceleration of ab initio molecular dynamics to quantification of aneurysm risk from blood hydrodynamics. The data scientists contributed tools and expertise in areas such as deep learning, Gaussian process regression, and sequential learning with which to engage these problems. Participants lived and worked together, collaboratively “hacked” for several hours per day, delivered introductory, midpoint, and final presentations and were exposed to presentations and informal interactions with NSF personnel. Social events were organized to facilitate interactions between teams. The primary outcomes of the event were to seed new collaborations between materials and data scientists and generate preliminary results. A separate competitive process enabled participants to apply for exploratory funding to continue work commenced at the hackathon. Anonymously surveyed participants reported a high level of satisfaction with the event, with 100% of respondents indicating that their team will continue to work together into the future and 91% reporting intent to submit a white paper for exploratory funding.
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Conference report: 2018 materials and data science hackathon (MATDAT18)
The National Science Foundation (NSF) 2018 Materials and Data Science Hackathon (MATDAT18) took place at the Residence Inn Alexandria Old Town/Duke Street, Alexandria, VA over the period May 30–June 1, 2018. This three-day collaborative “hackathon” or “datathon” brought together teams of materials scientists and data scientists to collaboratively engage materials science problems using data science tools. The materials scientists brought a diversity of problems ranging from inorganic material bandgap prediction to acceleration of ab initio molecular dynamics to quantification of aneurysm risk from blood hydrodynamics. The data scientists contributed tools and expertise in areas such as deep learning, Gaussian process regression, and sequential learning with which to engage these problems. Participants lived and worked together, collaboratively “hacked” for several hours per day, delivered introductory, midpoint, and final presentations and were exposed to presentations and informal interactions with NSF personnel. Social events were organized to facilitate interactions between teams. The primary outcomes of the event were to seed new collaborations between materials and data scientists and generate preliminary results. A separate competitive process enabled participants to apply for exploratory funding to continue work commenced at the hackathon. Anonymously surveyed participants reported a high level of satisfaction with the event, with 100% of respondents indicating that their team will continue to work together into the future and 91% reporting intent to submit a white paper for exploratory funding.
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- Award ID(s):
- 1748198
- PAR ID:
- 10095265
- Date Published:
- Journal Name:
- Molecular Systems Design & Engineering
- ISSN:
- 2058-9689
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9me90018g
