Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

• Published in: npj computational materials 2023-03, Vol.9 (1), p.31-8, Article 31
• Main Authors: Szymanski, Nathan J., Bartel, Christopher J., Zeng, Yan, Diallo, Mouhamad, Kim, Haegyeom, Ceder, Gerbrand
• Format: Article
• Language: English
• Subjects: 639/301/1034/1037; 639/301/930/12; Algorithms; Automation; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Decision making; Deep learning; Effectiveness; Electron microscopy; Experimentation; Experiments; Identification; Information retrieval; Learning algorithms; Machine learning; MATERIALS SCIENCE; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Neural networks; Theoretical; X-ray diffraction
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-00984-y

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.