Lecture
The role of analytics for closing the loop in Self-Driving Labs
- at -
- ICM Saal 4b
- Type: Lecture
Lecture description
The recent emergence of self-driving laboratories (SDL) and material acceleration platforms (MAPs) demonstrates the ability of these systems to change the way chemistry and material syntheses will be performed in the future. Especially in conjunction with
nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting contributions. Consequently, new tools that enhance the development and optimization
cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By automating the processing and characterization steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility, reproducibility, and flexibility of the platform.
The system also incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). In general, the interface with data analysis algorithms from in-line, at-line, and off-line measurements is of great importance for closing the design-make-test-analyze cycle and using these platforms efficiently. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL.
Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly
graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the
workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.
These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future.
nano- and advanced materials which are generally recognized for their great potential in solving current material science challenges, such systems can make disrupting contributions. Consequently, new tools that enhance the development and optimization
cycle of nano- and advanced materials are crucial. In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials [1], that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By automating the processing and characterization steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility, reproducibility, and flexibility of the platform.
The system also incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). In general, the interface with data analysis algorithms from in-line, at-line, and off-line measurements is of great importance for closing the design-make-test-analyze cycle and using these platforms efficiently. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL.
Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly
graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the
workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.
These features underscore the SDL’s potential as a transformative tool for advancing and accelerating the development of nano- and advanced materials, offering solutions for a sustainable and environmentally responsible future.
Literature:
[1] M. Zaki, C. Prinz, B. Ruehle, ACS Nano 2025, 19(9), 9029-9041
[2] B. Rühle, J. F. Krumrey, V. D. Hodoroaba, Sci Rep 2021, 11, 4942
[3] B. Ruehle, Digital Discovery 2025, 4, 1534 - 1543
