Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Maria A. Butakova, Andrey V. Chernov, Oleg O. Kartashov, Alexander V. Soldatov
2021 Nanomaterials  
Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters' experience. Self-driving laboratories help automate and intellectualize processes involved in
more » ... nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter's behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.
doi:10.3390/nano12010012 pmid:35009962 pmcid:PMC8746699 fatcat:gwplcxrxlbgj5fgusfzwgeuoie