Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale.Despite the significant progress in experiment and theory,however,most investigations on ferroelectric domain walls are still on a fundamental level,and reliable characterization of emergent transport phenomena remains a challenging task.

Machine learning for automated experimentation in scanning transmission electron microscopy

Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and closed-loop microscope operation.The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization.Here,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop operations.Specifically,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple platforms.These considerations will collectively inform the operationalization of ML in next-generation experimentation.

Symmetry-aware recursive image similarity exploration for materials microscopy

In pursuit of scientific discovery,vast collections of unstructured structural and functional images are acquired;however,only an infinitesimally small fraction of this data is rigorously analyzed,with an even smaller fraction ever being published.One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted.Unfortunately,data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives.Moreover,there are no robust methods to search unstructured databases of images to deduce correlations and insight.Here,we develop a machine learning approach to create image similarity projections to search unstructured image databases.To improve these projections,we develop and train a model to include symmetry-aware features.As an exemplar,we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years.We demonstrate how this tool can be used for interactive recursive image searching and exploration,highlighting structural similarities at various length scales.This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations.We provide a customizable open-source package(https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer)of this interactive tool for researchers to use with their data.