Large scale observatories are shared-use resources that provide open access to data from geographically distributed sensors and instruments. This data has the potential to accelerate scientific discovery. However, seamlessly integrating the data into scientific workflows remains a challenge. In this paper, we summarize our ongoing work in supporting data-driven and data-intensive workflows and outline our vision for how these observatories can improve large-scale science. Specifically, we present programming abstractions and runtime management services to enable the automatic integration of data in scientific workflows. Further, we show how approximation techniques can be used to address network and processing variations by studying constraint limitations and their associated latencies. We use the Ocean Observatories Initiative (OOI) as a driving use case for this work.
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Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery
The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self‐driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access to a larger part of the chemical universe and reduce the time‐to‐solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data‐centric abstraction to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self‐driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self‐driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.
- Award ID(s):
- 1940959
- NSF-PAR ID:
- 10418720
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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