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

Chan, Henry; Sasikumar, Kiran; Srinivasan, Srilok; Cherukara, Mathew; Narayanan, Badri; Sankaranarayanan, Subramanian K. R. S.

doi:10.1039/C9NR02873K

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

Journal Article · Mon May 20 00:00:00 EDT 2019 · Nanoscale

DOI:https://doi.org/10.1039/C9NR02873K· OSTI ID:1526638

^[1]; Sasikumar, Kiran ^[1]; Srinivasan, Srilok ^[1];

^[1];

^[2];

^[3]

Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Louisville, Louisville, KY (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357; 2016-082-R1; AC02-05CH11231

OSTI ID:: 1526638

Alternate ID(s):: OSTI ID: 1514737

Journal Information:: Nanoscale, Vol. 11, Issue 21; ISSN 2040-3364

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 18 works

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