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Title: Automated generation of machine learning-based atomistic potentials for extreme conditions

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1605115
Report Number(s):
LA-UR-20-22374
DOE Contract Number:  
89233218CNA000001
Resource Type:
Conference
Resource Relation:
Conference: National Meeting of the American Physical Society ; 2020-03-02 - 2020-03-06 ;
Country of Publication:
United States
Language:
English
Subject:
Material Science

Citation Formats

Nebgen, Benjamin Tyler, Smith, Justin Steven, Mathew, Nithin, Chen, Jie, Burakovsky, Leonid, Fensin, Saryu Jindal, and Barros, Kipton Marcos. Automated generation of machine learning-based atomistic potentials for extreme conditions. United States: N. p., 2020. Web.
Nebgen, Benjamin Tyler, Smith, Justin Steven, Mathew, Nithin, Chen, Jie, Burakovsky, Leonid, Fensin, Saryu Jindal, & Barros, Kipton Marcos. Automated generation of machine learning-based atomistic potentials for extreme conditions. United States.
Nebgen, Benjamin Tyler, Smith, Justin Steven, Mathew, Nithin, Chen, Jie, Burakovsky, Leonid, Fensin, Saryu Jindal, and Barros, Kipton Marcos. Fri . "Automated generation of machine learning-based atomistic potentials for extreme conditions". United States. https://www.osti.gov/servlets/purl/1605115.
@article{osti_1605115,
title = {Automated generation of machine learning-based atomistic potentials for extreme conditions},
author = {Nebgen, Benjamin Tyler and Smith, Justin Steven and Mathew, Nithin and Chen, Jie and Burakovsky, Leonid and Fensin, Saryu Jindal and Barros, Kipton Marcos},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}

Conference:
Other availability
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