Open Access

The rapid rise of self-driving laboratories (SDLs) and materials acceleration platforms (MAPs) is reshaping how chemical and materials syntheses may be carried out in the future. When combined with nano- and advanced materials, which are widely regarded as key enablers for addressing pressing challenges in materials science, these platforms have the potential to deliver truly disruptive advances. As a result, there is a strong need for new tools that significantly accelerate the development and optimization cycles of such materials. In this talk, we present our Self-Driving Laboratory (SDL) for Nano and Advanced Materials [1], which integrates robotic, batch-based autonomous synthesis with automated purification, characterization, and data analysis to provide a complete and reliable nanomaterial synthesis workflow. The platform enables end-to-end synthesis, from molecular precursors to fully purified nanomaterials. To demonstrate its versatility, reproducibility, and flexibility, we automated processing and characterization workflows for seven materials spanning five fundamentally different classes of nano- and advanced materials: metals, metal oxides, silica, metal–organic frameworks, and core–shell particles, each governed by distinct reaction mechanisms. The SDL further incorporates in-line characterization capabilities, including measurements of hydrodynamic diameter, zeta potential, and optical properties such as absorbance and fluorescence. Seamless integration of data analysis algorithms for in-line, at-line, and off-line measurements is critical for closing the design-make-test-analyze loop and for efficiently operating such platforms. We will present examples showing how AI-assisted automatic image segmentation of electron microscopy data [2] can help alleviate the “data analysis bottleneck” associated with off-line characterization. In addition, we will discuss the machine learning algorithms currently implemented in the system backend, which enable ML-driven, closed-loop optimization of material properties. Finally, we highlight recent efforts [3] aimed at improving the usability of SDLs by simplifying workflow generation. These include the use of large language models to automatically translate synthesis procedures written in natural language into executable workflows, as well as intuitive graphical user interfaces based on node editors. These tools also enable the extraction of knowledge graphs from workflows. In parallel, we are developing a unified description and ontology for process steps and parameters, which supports semantic representation and interoperability of workflows across different SDL hardware and software platforms [4]. Together, these capabilities position our SDL as a powerful and transformative platform for accelerating the development of nano- and advanced materials, contributing to more sustainable and environmentally responsible materials innovation.

Die für die Sicherheit von Mensch und Umwelt sowie für die Messtechnik zuständigen deutschen Bundesbehörden – das Umweltbundesamt, die Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA), das Bundesinstitut für Risikobewertung (BfR), die Bundesanstalt für Materialforschung und -prüfung (BAM) und die Physikalisch-Technische Bundesanstalt (PTB) – begleiten die rasante Innovation von neuartigen Materialien mit einer langfristigen Forschungsstrategie. Die vorgestellte Strategie zeigt die notwendige Sicherheitsforschung auf, die auf der einen Seite Regulierungsbehörden in die Lage versetzt, neuartige Materialien angemessen bewerten zu können, und auf der anderen Seite auch das „Safe and Sustainable by Design“ für Innovatoren unterstützt.

The German federal authorities responsible for human and environmental safety, and metrology – the German Environment Agency (UBA), the Federal Institute for Occupational Safety and Health (BAuA), the German Federal Institute for Risk Assessment (BfR), the Federal Institute for Materials Research and Testing (BAM), and the National Metrology Institute (Physikalisch-Technische Bundesanstalt, PTB) - are accompanying the rapid pace of innovation of advanced materials with a long term research strategy focusing on safety research needs from a regulatory perspective. The strategy builds on former joint research strategies on nanomaterials and advanced materials and highlights current research priorities to enable regulatory preparedness for material innovations and better connect safety research with innovation research.

Representing experimental procedures in an unambiguous way that can be understood and reproduced by other scientists is at the heart of scientific progress. For centuries, these descriptions were made by humans and for humans, often assuming implicit or tacit knowledge. However, when Materials Acceleration Platforms (MAPs) and Self-Driving Labs (SDLs) are used for the autonomous discovery and optimization of materials, sharing knowledge, and workflows that were designed and executed by machines becomes increasingly important. These machines require an explicit, precise and accurate description and modeling of all process parameters and steps that need to be executed. To address these needs, especially in the domain of materials science and nano and advanced materials synthesis, we developed the Wet Chemical Synthesis Ontology (WCSO), which is based on the Platform MaterialDigital core ontology (PMDco) and the Basic Formal Ontology (BFO). The ontology contains recurring concepts from millions of wet chemical synthesis procedures in the scientific literature. We discuss the design considerations, concepts, and architecture of our ontology in detail, and demonstrate how it can be applied to the construction and querying of semantically annotated knowledge graphs from wet chemical nano- and advanced materials synthesis workflows that were previously designed for and then executed on an SDL. Using such formal representations and semantic annotations for describing synthesis procedures and workflows facilitates the reproducibility, sharing, and execution of synthesis procedures across different labs around the world that use different orchestrators for their robotic hardware.

In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials, 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 fully automating the processing 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 and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI 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 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.