Ut-Battelle, Inc.

Rama K Vasudevan

Sergei V Kalinin

Yongtao Liu

Kevin Roccapriore

Maxim A Ziatdinov

Jacob D Hinkle

Kyle P Kelley

Olga S Ovchinnikova

Anton V Ievlev

Nikolay Borodinov

Systems are provided for machine learning-driven operation of instrumentation with human in the loop. The systems use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems use active learning, which considers selection, reinforcement and/or adjustment inputs from the instrumentation's user, to enable describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.

Machine learning-driven operation of instrumentation with human in the loop

Systems, methods and programs are provided for automated science experiments which use a model with learnt model parameters to define points for physical-characteristic measurements once the model is trained. The systems, methods and programs use active learning which enables describing a relationship between local features of sample-surface structure shown in image patches and determined representations of physical-characteristic measurements.

Science-driven automated experiments

Techniques for generating full-spatial resolution, full spectral resolution image(s) from a 3D spectral-data cube for any spectral value within a given spectral range are provided without requiring the acquisition of all full-spatial resolution, full spectral resolution data by an instrument. The 3D spectral-data cube is generated from a limited number of full-spatial resolution, sparse spectral resolution data and a sparse-spatial resolution, full-spectral resolution data of the same area of the sample. The use of the 3D spectral-data cube reduces the data acquisition time.

Pan-sharpening for microscopy

Growing community of inventors

Oak Ridge, TN, United States of America

Rama K Vasudevan