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The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations

npj Computational Materials 2022年第1期8卷 236-244页

作者： Nakib H.Protik Chunhua Li Miguel Pruneda David Broido Pablo Ordejón Catalan Institute of Nanoscience and Nanotechnology(ICN2) CSIC and BISTCampus Bellaterra8193BarcelonaSpain Department of Physics Boston CollegeChestnut HillBostonMA02467USA

elphbolt is a modern Fortran (2018 standard) code for efficiently solving the coupled electron–phonon Boltzmann transport equations from first principles.Using results from density functional and density functional perturbation theory as inputs,it can calculate the effect of the non-equilibrium phonons on the electronic transport (phonon drag) and non-equilibrium electrons on the phononic transport (electron drag) in a fully self-consistent manner and obeying the constraints mandated by thermodynamics.It can calculate the lattice,charge,and thermoelectric transport coefficients for the temperature gradient and electric fields,and the effect of the mutual electron–phonon drag on these transport properties.The code fully exploits the symmetries of the crystal and the transport-active window to allow the sampling of extremely fine electron and phonon wave vector meshes required for accurately capturing the drag phenomena.The coarray feature of modern Fortran,which offers native and convenient support for parallelization,is utilized.The code is compact,readable,well-documented,and extensible by design.

关键词： properties phonon equations

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Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

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npj Computational Materials 2023年第1期9卷 2000-2007页

作者： Yu Xie Jonathan Vandermause Senja Ramakers Nakib H.Protik Anders Johansson Boris Kozinsky John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMAUSA Department of Physics Harvard UniversityCambridgeMAUSA Corporate Sector Research and Advance Engineering Robert Bosch GmbHRenningenUSA Interdisciplinary Centre for Advanced Materials Simulation Ruhr-Universität BochumBochumGermany Robert Bosch LLC Research and Technology CenterWatertownMAUSA

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been recently developed to train force fields efficiently and automatically.Among them,Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions.In this work,we present a general Bayesian active learning workflow,where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors.To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation,we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude.We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide(SiC)polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured.The resulting model exhibits close agreement with both ab initio calculations and experimental measurements,and outperforms existing empirical models on vibrational and thermal properties.The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

关键词： computer thermal utilize

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Author Correction:The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations

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npj Computational Materials 2022年第1期8卷 508-508页

作者： Nakib H.Protik Chunhua Li Miguel Pruneda David Broido Pablo Ordejón Catalan Institute of Nanoscience and Nanotechnology (ICN2) CSIC and BIST Campus Bellaterra 8193 Barcelona Spain Department of Physics Boston College Chestnut Hill Boston MA 02467 USA

The original version of this article contained typographical errors in Equation(6)and Equation(7).

关键词： Equation phonon graphical

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how dopants limit the ultrahigh thermal conductivity of boron arsenide:a first principles study

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npj Computational Materials 2021年第1期7卷 491-497页

作者： Mauro Fava nakib haider protik Chunhua Li Navaneetha Krishnan Ravichandran Jesús Carrete Ambroise van Roekeghem Georg K.h.Madsen Natalio Mingo David Broido UniversitéGrenoble Alpes Saint-Martin-d’HèresFrance John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMAUSA Department of Physics Boston CollegeChestnut HillMAUSA Department of Mechanical Engineering Indian Institute of ScienceBangaloreIndia Institute of Materials Chemistry TU WienViennaAustria CEA LITENGrenobleFrance

The promise enabled by boron arsenide’s(BAs)high thermal conductivity(κ)in power electronics cannot be assessed without taking into account the reduction incurred when doping the material.Using first principles calculations,we determine theκreduction induced by different group IV impurities in BAs as a function of concentration and charge state.We unveil a general trend,where neutral impurities scatter phonons more strongly than the charged ones.CB and GeAs impurities show by far the weakest phonon scattering and retain BAsκvalues of over~1000W⋅K^(−1)⋅m^(−1) even at high densities.Both Si and Ge achieve large hole concentrations while maintaining highκ.Furthermore,going beyond the doping compensation threshold associated to Fermi level pinning triggers observable changes in the thermal conductivity.This informs design considerations on the doping of BAs,and it also suggests a direct way to determine the onset of compensation doping in experimental samples.

关键词： principles boron scatter

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