Lab-wide Initiative
Please note: Only FY22 Carbon Negative Initiative continuation proposals will be considered for Lab-wide Initiative Track funding in FY23. New proposals should not be submitted and will not be reviewed under this track.
Carbon Negative Initiative (CNI)
POC: Bill Collins
This initiative was previously titled Negative Emissions Science and Technology (NEST). The Lab seeks proposals to explore how Berkeley Lab could contribute to the grand challenge of removing greenhouse gases (GHGs) from the climate system via investigations into the scientific and technical foundations for Negative Emission Technologies (NETs). According to the Intergovernmental Panel on Climate Change, addressing global warming will require rapid and widespread deployment of NETs to remove anthropogenic carbon dioxide and other GHGs from the climate system. This initiative will identify the most promising directions for future NETs research, leveraging the unique strengths of Berkeley Lab. Responsive proposals will act to:
- Identify and demonstrate the scientific foundations for new NETs;
- Find pathways to accelerate and optimize promising NETs with high potential; and/or
- Develop novel systems engineering approaches for capturing, handling, and storing carbon dioxide and it into biofuels, bioproducts, and biomaterials.
See Call for Proposals for additional information.
Multi-Area Topics
Berkeley Lab leadership encourages multi-Area LDRD proposals developing capabilities in general domains. The intent of this track is to encourage and support research initiatives that are being pursued across Areas and that incorporate one of the three topic areas:
Automation in the Acquisition and Management of Experimental Data (Self-Driving Labs)*
POCs: Nigel Mouncey, Junko Yano
The Lab seeks proposals that investigate, develop, and use technologies to automate discovery science, including through the next generation of advanced instrumentation and user facilities/centers at Berkeley Lab. The proposals may approach this from:
- The fundamental stage: i.e., building out core level technologies and tools to assist with general lab automation
- The applied perspective: i.e., reimagining the application of lab automation tools to research
- A combination of the two
Data Science/Machine Learning to Accelerate Science*
POCs: Junko Yano, Nigel Mouncey
The Lab seeks proposals that build on (and extend) innovative approaches to the management and use of datasets to extend our understanding of underlying science in areas of importance to Berkeley Lab. The proposals may approach this from:
- The fundamental stage: i.e., building out core level algorithms and methods to enhance data science, analysis, and machine learning
- The applied perspective: i.e., applying or re-engineering existing DS/ML methods and techniques to solve applied problems in novel ways
- A combination of the two
Instrumentation to Advance Fundamental and Applied Science
POC: Junko Yano
The Lab seeks proposals to develop innovative instrumentation in sensing, measurement, readout and data acquisition that will advance fundamental and/or applied scientific opportunities at Berkeley Lab. Multi-Area collaborations are encouraged in pursuit of:
- Advanced instrumentation to bring unique capabilities to bear on priority scientific research
- Needed investments in technical infrastructure in support of instrumentation
See Call for Proposals for additional information.
Project Requirements & Opportunities
- Proposals should have a lead Division/Area PI who will submit the proposal & are responsible for submitting a full budget for ALL participating Areas.
- Proposals need to undergo coordinated review with leadership from ALL involved Science Areas.
- Funding will be contributed by each PI’s/Co-PI’s home Area carve-out based on the technical contributions from each Area.
- Up to a maximum of $100K of total pre-site support funding will be contributed by the Directorate for a select number of Multi-Area projects (as determined by Lab leadership), with the possibility of more in very exceptional cases.