TY - CONF A1 - Ruehle, Bastian T1 - The Role of Analytics for Closing the Loop in Self-Driving Labs N2 - The recent emergence of self-driving laboratories (SDL) and material acceleration plat-forms (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 con-tributions. 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 nano-materials – with automated characterization and data analysis, for a complete and reli-able nanomaterial synthesis workflow. By automating the processing and characteriza-tion 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 measure-ments 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 seg-mentation of electron microscopy images with the help of AI [2] can be used for reduc-ing 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 com-mon 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. T2 - Analytica Conference 2026 CY - Munich, Germany DA - 24.03.2026 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation KW - Digitalization KW - Ontologies PY - 2026 AN - OPUS4-65760 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust data generation, heuristics and machine learning for materials design N2 - Machine learning (ML) offers new routes to overcome the limitations of density functional theory (DFT) for advanced materials. We present data-generation strategies and workflows for ML interatomic potentials, including large-scale quantum-chemical bonding analysis.[1,2,3] Incorporating bonding descriptors into ML models enables prediction of phononic properties and validation of correlations between bonding strength, force constants, and thermal conductivity.[3] We introduce autoplex, an automated framework for training ML potentials, supporting general-purpose and phonon-focused workflows.[4] These developments provide a basis for fine-tuning foundation models for thermal transport at reduced cost.[5] For properties such as magnetism or synthesizability, we discuss complementary approaches, comparing ab initio methods with chemical heuristics and experimental data-driven ML models.[6,7]Our work advances scalable, accurate simulations for materials discovery. T2 - DPG Dresden, Condensed Matter CY - Dresden, Germany DA - 10.03.2026 KW - Materials Design KW - Materials Acceleration Platforms KW - Advanced materials simulations KW - Automation KW - High-throughput KW - Thermal Conductivity KW - Chemical Bonding Analysis KW - Phonons KW - Amorphous Materials PY - 2026 AN - OPUS4-65648 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ryll, Tom William T1 - Multi-modal characterization for transformation of gypsum to anhydrite N2 - We investigate recycling of gypsum waste materials and have developed a method to synthesize phase pure anhydrite in solution, while contaminants could easily be separated from products. In-situ XRD measurements were conducted in a dedicated automation setup at BESSY II to find exact reaction conditions and were supported by in-situ Raman spectroscopy. T2 - Joint BAM-HZB Symposium on Multimodal Synchrotron Experiments for Next-Generation Materials Research CY - Berlin, Germany DA - 25.02.2026 KW - Recycling KW - Automation KW - Synchrotron XRD KW - Raman spectroscopy KW - Gypsum PY - 2026 AN - OPUS4-65607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano and Advanced Materials Synthesis in a Self-Driving Lab N2 - 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. T2 - Series on Digitalisation-Meet the Experts | Special Topic: Automation CY - Berlin, Germany DA - 27.02.2026 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation KW - Digitalization PY - 2026 AN - OPUS4-65604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Crossing Scientific Disciplines with Materials Informatics:� From Atoms to Algorithms N2 - Within this talk, I introduced students from Physics to Materials Informatics. To provide a context for this research, I have introduced the students to BAM and its tasks. I then started to introduce our activity field materials design, including materials acceleration platforms. Then, I explained how simulations speed up the materials searches as parf of materials acceleration platforms. T2 - jDPG Jena Meeting - Poland exchange CY - Jena, Germany DA - 26.02.2026 KW - Automation KW - Machine Learning KW - Materials Design KW - Batteries KW - Workflows PY - 2026 AN - OPUS4-65589 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liu, Yuanbin A1 - Zhou, Yuxing A1 - Ademuwagun, Richard A1 - Walterbos, Luc A1 - George, Janine A1 - Elliott, Stephen R. A1 - Deringer, Volker L. T1 - Medium-Range Structural Order in Amorphous Arsenic N2 - Medium-range order (MRO) is a key structural feature of amorphous materials, but its origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (a-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structure factor of a-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of a-As with that of its lighter homologue, red amorphous phosphorus (a-P): we find that a-As has a more uniform dihedral-angle distribution, and so we confirm that its structure can be thought of as a 3-fold coordinated continuous random network in first approximation, in contrast to the more molecular-cluster-like structure of a-P. The pressure-dependent structural behaviors of a-As and a-P differ as well, and the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations. KW - Machine Learned Interatomic Potentials KW - Amorphous KW - Advanced Materials KW - Automation KW - Digitalisation KW - Structure-Property- Relationships PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-655952 DO - https://doi.org/10.1021/jacs.5c18688 SN - 0002-7863 SP - 1 EP - 13 PB - American Chemical Society (ACS) AN - OPUS4-65595 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - This talk first introduces students to the Materials Acceleration Platforms and Advanced Materials Characterization at BAM. Then, it motivates high-throuhgput screening for materials discovery and advanced materials simulations based on these core topics. Then four different research studies are presentend: evaluation of generative models, synthesizability prediction via PU learning, acceleration of materials property predictions with bonding analysis and advanced materials simulations supported by automatically trained machine learning potentials. T2 - Guest Lecture in MSE 403/1003, a Seminar in the Curriculum of the University of Toronto CY - Online meeting DA - 13.02.2026 KW - Automation KW - Materials Acceleration Platforms KW - Machine Learning KW - Workflows KW - Phonons KW - Bonding Analysis KW - Thermal Conductivity PY - 2026 AN - OPUS4-65514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - George, Janine A1 - Ouellet-Plamondon, Claudiane A1 - Reyes, Kristofer T1 - Introduction to the “Accelerate Conference 2023–2024” themed collection N2 - The collection showcases the ways in which automation, machine learning and robotics are transforming experimental materials science and chemistry into continuous, computationally integrated processes. It features innovations regarding autonomous laboratories, Bayesian optimisation, high-throughput experimentation and computation, and AI-driven literature extraction, which simplify and scale up materials discovery. Together, these works outline a modular, responsible framework for accelerating scientific progress through human-guided, data-driven autonomy. KW - Automation KW - Materials Acceleration Platforms KW - Synthesizability KW - Workflows KW - Large Language Models KW - Ontologies KW - Materials Discovery PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-654658 DO - https://doi.org/10.1039/d5dd90057c SN - 2635-098X SP - 1 EP - 2 PB - Royal Society of Chemistry (RSC) AN - OPUS4-65465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials. T2 - Seminar Gruppe Stephan Roche CY - Barcelona, Spain DA - 22.01.2026 KW - Automation KW - Machine Learning KW - Materials Acceleration Platforms KW - Thermal Conductivity KW - Phonons KW - Bonding Analysis PY - 2026 AN - OPUS4-65428 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials. T2 - Workshop on AI in Sustainable Materials Science CY - Düsseldorf, Germany DA - 27.01.2026 KW - Automation KW - Digitalisation KW - Materials Design KW - Thermal Conductivity KW - Chemical bonding KW - Materials Acceleration Platforms PY - 2026 AN - OPUS4-65427 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -