Integrated approaches that combine high-throughput experimentation, laboratory automation, and artificial intelligence have the potential to significantly accelerate the discovery of materials for sustainable technologies, including photovoltaics, energy storage, thermoelectrics, and catalysis. This symposium presents an overview of recent advances in such integrated methodologies for sustainable materials discovery. We highlight progress across the full pipeline, from hardware (e.g., robotic synthesis and characterization platforms) to software (e.g., optimization agents and large language models), and how these components contribute to accelerated discovery across diverse materials classes. Particular attention is given to strategies for addressing persistent challenges in closing the experimental loop, such as reliable sample transfer between stations (e.g., synthesis-to-characterization) and resolving geometry mismatches between synthesized samples and measurement requirements (e.g., powder outputs when pelletized samples are needed). By bringing together researchers from multiple disciplines, this symposium aims to foster cross-domain exchange and advance pathways for sustainable materials discovery
- Autonomous labs
- Energy materials
- Polymer-based materials for engineering applications
- Catalysis
- Artificial Intelligence & Machine Learning
- High-throughput experiments
- Robotics & Automation
- AI Agents & Large Language Models
Martin Aaskov KarlsenLeonard Ng Wei Tat is an Assistant Professor at the School of Materials Science and Engineering, Nanyang Technological University (NTU), Singapore, a Cluster Director at the Energy Research Institute @ NTU (ERI@N), PI at TUMCreate’s Proteins4Singapore project and deputy director of the AI for Materials Initiative (AIM). He leads the NGenuity Lab, a multidisciplinary research group of approximately eleven researchers working at the intersection of artificial intelligence, automation, and advanced materials. His research focuses on self-driving laboratories for accelerated materials discovery, AI-driven optimisation of photovoltaic devices (perovskite and organic solar cells), autonomous hydrometallurgical processing of e-waste, biomaterial scaffold design for cultivated meat and protein extraction from alternative protein sources such as soybean and microalgae. Leonard obtained his PhD in Engineering from the University of Cambridge, an MSc in Materials Science from the University of Leeds, a BSc in Political Science from Singapore Management University, and an MSc in Computer Science from Georgia Institute of Technology. He is a Member of the Royal Society of Chemistry (MRSC).
Özlem Özcan Sandikcioglu, Head of Division, Material and Surface Technologies, Federal Institute for Materials Research and Testing (BAM)
Özlem has an academic background in electrochemistry and material science. She focusses on the application of electrochemical methods for synthesis and characterisation of functional materials for corrosion protection and green electrocatalysis. Her research revolves mainly around metals and alloys, in all forms, as bulk materials, thin films and nanoparticles. A special focus of her research are multi-principal element alloys (MPEAs) which break from the conventional alloy concepts by offering a near infinite compositional space to explore for application-tailored properties. To tackle this complex design challenge, she relies on Material Acceleration Platforms (MAPs) and AI-guided autonomous material discovery. Özlem is also coordinating the MAP-activities at the Federal Institute of Materials Research and Testing (BAM).