| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 17, 2023 |
| Latest Amendment Date: | August 17, 2023 |
| Award Number: | 2335908 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Xiaogang (Cliff) Wang
xiawang@nsf.gov (703)292-2812 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2023 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $75,903.00 |
| Total Awarded Amount to Date: | $75,903.00 |
| Funds Obligated to Date: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4333 Brooklyn Ave NE Seattle WA US 98195-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Information Technology Researc |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Laboratory automation increases precision and efficiency of science experiments. Advances in low-cost sensors, actuators, robotic systems, and control systems have lowered the barrier to entry to laboratory automation such that fully self-driving labs will have the potential to enable new practices of science experiment and to accelerate scientific exploration progress. There is a critical need to develop the principles and methodologies for self-driving laboratories. These systems will likely draw from best practices and experiences learned in data science, human-machine interaction, manufacturing and quality control, open-source ecosystems, and laboratory science methods.
The proposed workshop will convene leaders in self-driving laboratories and related areas including data science, robotics, manufacturing, and open-source ecosystems to define a roadmap for self-driving laboratories. Experts will discuss on latest advances and current challenges in automated sample preparation, experiment generation, data collection, and data analysis. The workshop will help identify major themes and assess on how future, interconnected goals can be best supported in a research context. By convening community leader around related topics, the workshop will seed cross-discipline collaboration on infrastructure that can support a broad range of sciences, which may include more complex experiments to accelerate scientific discovery and perform cost effective verification and validation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The NSF Workshop on Self-Driving Laboratories (SDLs), held on November 14-15, 2023, at Georgia Tech, convened 23 participants from various universities, national laboratories, and government agencies. The workshop focused on four major themes: (1) mapping current efforts in SDLs, (2) identifying critical enabling infrastructure, (3) best practices for community and sustainability, and (4) open research questions.
Key Themes:
The following eight key themes were identified and discussed during the workshop:
- Mapping Current Efforts: Participants shared examples of SDLs, ranging from research prototypes to substantial operations, highlighting their applications, degree of automation, and scalability. Discussions on this effort endeavored to define a 10-year research roadmap for SDLs by understanding the current state of the art.
- Challenges and Needs: The workshop identified several challenges, including the need for advanced mathematical models, standardization of protocols, and integration of sensor technology. Capturing human expertise and developing robust models were also emphasized as critical needs.
- Current Examples: Promising SDL applications were discussed, such as automated biofoundries and mobile robots for specific scientific tasks. These examples demonstrated significant progress in automation and the potential of SDLs to revolutionize various fields.
- Critical Enabling Infrastructure: The importance of infrastructure for interoperability, connectivity, and data sharing was highlighted. Discussions focused on the need for standardized protocols, secure data sharing, and modular platforms to ensure scalability and flexibility.
- Workforce Development: The necessity of developing a trained workforce for SDLs was underscored. This includes creating educational programs that combine robotics, data science, and domain-specific sciences to equip individuals with the necessary skills.
- Incentivizing SDL Development: Strategies to encourage the development of SDL testbeds included financial incentives, public-private partnerships, and collaborative platforms. Recognition and accreditation programs were also suggested to motivate participation.
- Best Practices for Sustainability: Ensuring the sustainability of SDLs involves promoting resource efficiency, cross-disciplinary collaboration, and community engagement. Long-term planning and modular designs were recommended to maintain and expand SDL capabilities.
- Cybersecurity: Addressing cybersecurity challenges is crucial for SDLs. This includes implementing robust data security measures, network security protocols, and access control mechanisms to protect sensitive information and ensure system integrity.
Future Directions: Participants identified key areas for future development, such as closed-loop discovery processes and defining success metrics for SDLs. The workshop emphasized the importance of collaborative efforts and a clear vision to enhance human creativity and productivity through automation and advanced technologies.
Conclusions: The workshop concluded that the successful development of SDLs requires a balanced program of research, development, and application. Establishing robust partnerships among academia, industry, and government agencies will be foundational in advancing SDLs. By addressing the identified challenges and leveraging collaborative opportunities, SDLs can become integral components of research and development across disciplines, driving significant advancements in scientific discovery and industrial processes.
Last Modified: 04/25/2025
Modified by: Nadya Peek
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