Autonomous robotic experimentation system for powder X-ray diffraction

Abstract

The automation of materials research is essential for accelerating scientific discovery. Powder X-ray diffraction (PXRD) plays a crucial role in analyzing crystal structures and quantifying phase compositions in materials science. However, current methods face challenges in reproducibility and efficiency. To address these issues, we developed an autonomous robotic experimentation (ARE) system for PXRD that integrates the entire process from sample preparation to data analysis. This system combines a robotic arm for precise sample preparation with machine learning-based techniques for automated data analysis. Our approach consistently produced high-quality samples with reduced background noise, achieving accuracy comparable to manual preparation techniques. We also investigated the relationship between sample quantity and analysis accuracy, demonstrating the system's ability to obtain reliable results with significantly reduced sample amounts. This work advances laboratory automation capabilities and contributes to the development of autonomous materials discovery and optimization processes. By addressing key challenges in PXRD automation, our research enables more efficient and reproducible materials characterization methodologies.