Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials' nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image's uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis. JST CREST [JPMJCR1994]; Nanoscale Characterization and Fabrication Laboratory (NCFL), Institute for Critical Technology and Applied Science (ICTAS), Virginia Tech; NSF [ECCS 1542100,, ECCS 2025151]; US Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-FG02-06ER15786]; JSPS KAKENHI [19H02029, 20H02479] Published version M. Muramatsu and M. Murayama greatly appreciate the financial support by the JST CREST (JPMJCR1994). The authors also would like to express their special acknowledgement to Prof. Nobuhiro Tsuji (Kyoto University) and Chang-Yu Hung (Virginia Tech) for supporting results interpretation and data collection. This study was partly supported by Nanoscale Characterization and Fabrication Laboratory (NCFL), Institute for Critical Technology and Applied Science (ICTAS), Virginia Tech and used shared facilities at the Virginia Tech National Center for Earth and Environmental Nanotechnology Infrastructure (NanoEarth), a member of the National Nanotechnology Coordinated Infrastructure (NNCI), supported by NSF (ECCS 1542100, ECCS 2025151). M. Murayama acknowledges financial support from the US Department of Energy, Office of Science, Office of Basic Energy Sciences under Award #DE-FG02-06ER15786 for technical development of TEM in-situ deformation, and JSPS KAKENHI Grant Numbers 19H02029 & 20H02479.