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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Developing Methods, Models, and Datasets for a Self-Driving Magnetic Nanomaterials Laboratory: Application for Thermal Magnetic Particle Imaging
Author(s) Frank M. Abel, Paige Burke, Thinh Bui, Md Mahadi Rajib, Daniel Wines, Brian Donovan, Michelle Jamer, Solomon Woods, Kamal Choudhary
On-Site Speaker (Planned) Frank M. Abel
Abstract Scope Self-driving laboratories (SDLs) are emerging as a powerful approach for accelerating materials discovery, mapping material synthesis spaces, and improving experimental reproducibility. Realizing SDL workflows requires automating synthesis processes for robotic execution and developing automated data analysis tools for extracting quantitative information from characterization datasets. Despite significant progress in autonomous materials and nanoparticle synthesis platforms, comparatively little work has focused on SDL workflows for magnetic nanoparticles, a critical component of biomedical imaging technologies and new thermal magnetic imaging for semiconductor packaging. In this work, we developed a new combinatorial, automation-friendly synthesis method for phase-pure iron oxide nanoparticles. We complement this synthesis method with a machine learning model to predict the magnetization of iron oxide nanoparticles from X-ray diffraction data, a scriptable, Python-based XRD fitting method, and an unknown-peak detection algorithm. When coupled, these tools can reasonably assess if a given sample will have promising magnetic properties for thermal magnetic imaging.
Proceedings Inclusion? Planned:
Keywords Magnetic Materials, Machine Learning, Other

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