There is an urgent need to develop a data infrastructure comprising autonomous synthesis and characterization platforms that can manage and analyze the heterogeneous datasets that will be produced at massive volumes and rapid rates.
Success in autonomous experiments will critically depend on our ability to store, process, and analyze data to provide real-time feedback.
The goal of this project is to integrate physical laboratories with the data platform environment, allowing researchers to use AI methods to generate hypotheses based on previous experiments that can then be tested in the physical laboratory, while maintaining the resulting experimental data on the platform for future retrieval.
The project comprises two components:
- A cloud-based data platform with the following capabilities that connects distributed automated laboratories: infrastructure; data storage and access; and data extraction, aggregation, integration, and harmonization.
- Integration of physical laboratories with a data platform environment to allow for use of AI and machine learning methods to generate hypotheses based on previous experiments that could then be tested in the physical laboratory environment.
The deliverables and success of this project will reduce significant barriers for new or early-stage autonomous projects and enable data sharing and augmentation. As new instruments are connected to the platform, and further experiments are conducted, the additional datasets will be used to train additional AI models in self-driving lab experiments, accelerating impact and opening new research opportunities for autonomous scientific discovery.