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45 results found

Optical scattering measurements and simulation data for one-dimensional (1-D) patterned periodic sub-wavelength features

Data provided by  National Institute of Standards and Technology

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.

Tags: electromagnetic simulations,simulations,experimental,angle-resolved scattering,scattering,gratings,patterned semiconductors,semiconductors,scatterfield microscopy,bright-field microscopy,microscopy,inverse problems,machine learning,

Modified: 2024-02-22

Views: 0

QFlow 2.0: Quantum dot data for machine learning

Data provided by  National Institute of Standards and Technology

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.

Tags: machine learning,quantum dots,simulated data,

Modified: 2024-02-22

Views: 0

Nestor: a toolkit for quantifying tacit maintenance knowledge, for investigatory analysis in smart manufacturing

Data provided by  National Institute of Standards and Technology

There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.

Tags: information,communication,maintenance,tribal knowledge,event sequences,training,machine learning,data cleaning,prognostics,diagnostics,visualization,decision guidance,CMMS,scheduling,investigations,nestor,smart manufacturing,manufacturing operations,manufacturing performance,

Modified: 2024-02-22

Views: 0

REMI: Resource for Materials Informatics

Data provided by  National Institute of Standards and Technology

The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization.

Tags: machine learning,data analysis,data processing,materials science,materials genome initiative,

Modified: 2024-02-22

Views: 0

Dark solitons in BECs dataset 2.0

Data provided by  National Institute of Standards and Technology

Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical.

Tags: machine learning,Bose-Einstein condensates,dark solitons,

Modified: 2024-02-22

Views: 0