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Title: Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures

Abstract

Here, nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especiallymore » of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon–phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications.« less

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Louisville, Louisville, KY (United States)
  3. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1526638
Alternate Identifier(s):
OSTI ID: 1514737
Grant/Contract Number:  
AC02-06CH11357; 2016-082-R1; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Nanoscale
Additional Journal Information:
Journal Volume: 11; Journal Issue: 21; Journal ID: ISSN 2040-3364
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
77 NANOSCIENCE AND NANOTECHNOLOGY; 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING

Citation Formats

Chan, Henry, Sasikumar, Kiran, Srinivasan, Srilok, Cherukara, Mathew, Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures. United States: N. p., 2019. Web. doi:10.1039/C9NR02873K.
Chan, Henry, Sasikumar, Kiran, Srinivasan, Srilok, Cherukara, Mathew, Narayanan, Badri, & Sankaranarayanan, Subramanian K. R. S. Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures. United States. doi:10.1039/C9NR02873K.
Chan, Henry, Sasikumar, Kiran, Srinivasan, Srilok, Cherukara, Mathew, Narayanan, Badri, and Sankaranarayanan, Subramanian K. R. S. Mon . "Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures". United States. doi:10.1039/C9NR02873K. https://www.osti.gov/servlets/purl/1526638.
@article{osti_1526638,
title = {Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures},
author = {Chan, Henry and Sasikumar, Kiran and Srinivasan, Srilok and Cherukara, Mathew and Narayanan, Badri and Sankaranarayanan, Subramanian K. R. S.},
abstractNote = {Here, nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon–phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications.},
doi = {10.1039/C9NR02873K},
journal = {Nanoscale},
number = 21,
volume = 11,
place = {United States},
year = {2019},
month = {5}
}

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