ShengBTE: A solver of the Boltzmann transport equation for phonons

Abstract ShengBTE is a software package for computing the lattice thermal conductivity of crystalline bulk materials and nanowires with diffusive boundary conditions. It is based on a full iterative solution to the Boltzmann transport equation. Its main inputs are sets of second- and third-order interatomic force constants, which can be calculated using third-party ab-initio packages. Dirac delta distributions arising from conservation of energy are approximated by Gaussian functions. A locally adaptive algorithm is used to determine each process-specific broadening parameter, which renders the method fully parameter free. The code is free software, written in Fortran and parallelized using MPI. A complementary Python script to help compute third-order interatomic force constants from a minimum number of ab-initio calculations, using a real-space finite-difference approach, is also publicly available for download. Here we discuss the design and implementation of both pieces of software and present results for three example systems: Si, InAs and lonsdaleite. Program summary Program title: ShengBTE Catalogue identifier: AESL_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AESL_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 292 052 No. of bytes in distributed program, including test data, etc.: 1 989 781 Distribution format: tar.gz Programming language: Fortran 90, MPI. Computer: Non-specific. Operating system: Unix/Linux. Has the code been vectorized or parallelized?: Yes, parallelized using MPI. RAM: Up to several GB Classification: 7.9. External routines: LAPACK, MPI, spglib ( http://spglib.sourceforge.net/ ) Nature of problem: Calculation of thermal conductivity and related quantities, determination of scattering rates for allowed three-phonon processes Solution method: Iterative solution, locally adaptive Gaussian broadening Running time: Up to several hours on several tens of processors

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