(CLOSED) Data Science to Advance Chemical and Materials Sciences DE-FOA-0002474

Description of the Award: 

The DOE SC program in Basic Energy Sciences (BES) announces its interest in receiving new applications from teams of investigators expanding the integration of data science methods with BES research disciplines, to accelerate scientific discovery and overcome difficult challenges in these fields. This FOA is focused on new applications that will take advantage of the rapid growth of data science, including artificial intelligence (AI) and machine learning (ML) methodologies. The FOA will support teams of investigators for synergistic computational, experimental, and theoretical research covered by the research areas in the BES divisions of Chemical Sciences, Geosciences, and Biosciences (CSGB) and Materials Sciences and Engineering (MSE). The focus of the proposed research must be on science-based, data-driven approaches enabling solutions for fundamental basic energy sciences challenges not possible otherwise. The goal of the application should be to integrate novel data science, uncertainty quantification, and other AI and ML approaches with domain sciences to uniquely advance the understanding of fundamental properties and processes relevant to chemical and materials systems, and achieve predictability of functions and behavior under dynamic conditions.


Sponsor Final Deadline: 
Apr 12, 2021
To be considered as a Penn State institutional nominee, please submit a notice of intent by the date provided directly below.
Penn State OVPR NOI Deadline: 
Friday, April 9, 2021 - 12:00pm
This limited submission is in downselect: 
Penn State may only submit a specific number of proposals to this funding opportunity. The number of NOIs received require that an internal competition take place, thus, a downselect process has commenced. No Penn State researchers may apply to this opportunity outside of this downselect process. To apply for this limited submission, please use this link:
Zi-Kui Liu (E&MS); Darren Pagan (E&MS)