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Title: Learning to Predict Material Structure from Neutron Scattering Data

Abstract

Understanding structural properties of materials and how they relate to its atomic structure, while extremely challenging, is a key scientific quest that has dominated the landscape of materials research for decades. Neutron and X-ray scattering is a state-of-the-art method to investigate material structure on the atomic scale. Traditional methods of processing neutron scattering data to decipher the structure of target materials have relied on computing scattering patterns using physics-based forward models and comparing them with experimentally gathered scattering profiles within a computationally expensive optimization loop. Here, we report an initial design of a data-driven machine learning pipeline for material structure prediction that is computationally faster (once trained) and potentially more accurate. We describe the architecture of the ML pipeline and a preliminary benchmarking study of shallow machine learning models in terms of their prediction accuracy and limitations. We show that material structure prediction from neutron scattering data using shallow learning models is feasible to within 90% prediction accuracy for certain classes of materials but deeper models are required for more general material structure predictions.

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]
  1. Los Alamos National Laboratory (LANL)
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1651371
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 2019 - Los Angeles, California, United States of America - 12/9/2019 5:00:00 AM-
Country of Publication:
United States
Language:
English

Citation Formats

Garcia Cardona, Cristina, Kannan, Ramakrishnan, Johnston, Travis, Proffen, Thomas, Page, Katharine, and Seal, Sudip. Learning to Predict Material Structure from Neutron Scattering Data. United States: N. p., 2019. Web. doi:10.1109/BigData47090.2019.9005968.
Garcia Cardona, Cristina, Kannan, Ramakrishnan, Johnston, Travis, Proffen, Thomas, Page, Katharine, & Seal, Sudip. Learning to Predict Material Structure from Neutron Scattering Data. United States. https://doi.org/10.1109/BigData47090.2019.9005968
Garcia Cardona, Cristina, Kannan, Ramakrishnan, Johnston, Travis, Proffen, Thomas, Page, Katharine, and Seal, Sudip. 2019. "Learning to Predict Material Structure from Neutron Scattering Data". United States. https://doi.org/10.1109/BigData47090.2019.9005968. https://www.osti.gov/servlets/purl/1651371.
@article{osti_1651371,
title = {Learning to Predict Material Structure from Neutron Scattering Data},
author = {Garcia Cardona, Cristina and Kannan, Ramakrishnan and Johnston, Travis and Proffen, Thomas and Page, Katharine and Seal, Sudip},
abstractNote = {Understanding structural properties of materials and how they relate to its atomic structure, while extremely challenging, is a key scientific quest that has dominated the landscape of materials research for decades. Neutron and X-ray scattering is a state-of-the-art method to investigate material structure on the atomic scale. Traditional methods of processing neutron scattering data to decipher the structure of target materials have relied on computing scattering patterns using physics-based forward models and comparing them with experimentally gathered scattering profiles within a computationally expensive optimization loop. Here, we report an initial design of a data-driven machine learning pipeline for material structure prediction that is computationally faster (once trained) and potentially more accurate. We describe the architecture of the ML pipeline and a preliminary benchmarking study of shallow machine learning models in terms of their prediction accuracy and limitations. We show that material structure prediction from neutron scattering data using shallow learning models is feasible to within 90% prediction accuracy for certain classes of materials but deeper models are required for more general material structure predictions.},
doi = {10.1109/BigData47090.2019.9005968},
url = {https://www.osti.gov/biblio/1651371}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

Conference:
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