fairfolio
Saved Projects

NRDAA: A Machine Learning Algorithm for Fitting Neutron Reflectometry Data

ISEF · 2020 Systems Software

Seay, Veda, Brady, Andrew · Oak Ridge High School

Overview

Neutron reflectometry is a useful technique to analyze the structure of thin films, but the current methods for fitting models to experimental data are time consuming and require significant knowledge of the sample. A thin film sample might have multiple layers, varying thicknesses and chemical compositions, which are the parameters needed to fit experimental data. A machine learning algorithm, Neutron Reflectometry Data Analysis Algorithm (NRDAA), was created to fit neutron reflectometry data by finding and optimizing parameter values. First, NRDAA utilizes weighted k-Nearest Neighbor to rank model types. Then, for a given model, Stochastic Gradient Descent optimizes the individual parameters. Finally, , NRDAA calculates the accuracy of the optimized parameters with sensitivity analysis. NRDAA was incorporated into a graphical user interface (GUI) to provide a user friendly platform. The GUI allows users to easily customize parameters based on their knowledge of the sample. NRDAA was tested with three samples of real neutron reflectometry data to verify its efficacy. NRDAA allows a user with minimal knowledge of their thin film sample to quickly fit experimental neutron reflectometry data.

Competition history

Resources

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

Source: Regeneron International Science and Engineering Fair

Save projects to your library

Sign in with Google to keep track of projects you find interesting, organized into folders. An account also raises your daily allowance for “Has this been done?”, and lets you create a key for the MCP server with a much higher limit than anonymous use. Browsing stays public.

Continue with Google