摘要
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions.
基金
The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301
the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST
A.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).
