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 calc...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.展开更多
Density functional theory(DFT)has become a standard tool for the study of point defects in materials.However,finding the most stable defective structures remains a very challenging task as it involves the solution of ...Density functional theory(DFT)has become a standard tool for the study of point defects in materials.However,finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function.Hitherto,the approaches most commonly used to tackle this problem have been mostly empirical,heuristic,and/or based on domain knowledge.In this contribution,we describe an approach for exploring the potential energy surface(PES)based on the covariance matrix adaptation evolution strategy(CMA-ES)and supervised and unsupervised machine learning models.The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids.We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well.展开更多
基金This work was supported in part by the Office of Naval Research under MURI grant no.N00014-16-1-2436the Agence Nationale de la Recherche through project ANR-17-CE08-0044-01+1 种基金G.K.H.M.acknowledges funding from the Austrian Science Funds(FWF)under project CODIS(Grant no.FWF-I-3576-N36)We thank Nebil Katcho for providing us with the first version of the code used to compute the phonon-defect scattering rates.D.B.thanks Dr.John Lyons of the Naval Research Laboratory for helpful discussions.
文摘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.
基金The authors adknowtedge support from the Austfan Science Funds(PWF)under project CODIS(FWF3576-N36)Part of the cakuhtons were performed on the Mienna Sdemmhc Cluster un der the projact 1523306 CODS.
文摘Density functional theory(DFT)has become a standard tool for the study of point defects in materials.However,finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function.Hitherto,the approaches most commonly used to tackle this problem have been mostly empirical,heuristic,and/or based on domain knowledge.In this contribution,we describe an approach for exploring the potential energy surface(PES)based on the covariance matrix adaptation evolution strategy(CMA-ES)and supervised and unsupervised machine learning models.The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids.We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well.
