Color centers play key roles in,e.g.,solid state lighting and quantum information technology.Here,we describe an approach for predicting the optical line shapes of such emitters based on direct sampling of the underly...Color centers play key roles in,e.g.,solid state lighting and quantum information technology.Here,we describe an approach for predicting the optical line shapes of such emitters based on direct sampling of the underlying autocorrelation functions through molecular dynamics simulations(MD-ACF).The energy landscapes are represented by a machine-learned potential that describes both the ground and excited state landscapes through a single model,guaranteeing size-consistent predictions.We apply this methodology to the(V_(Si)V_(C))_(kk)^(0)divacancy defect in 4H-SiC and demonstrate that at low temperatures,the present MD-ACF approach reproduces results from the traditional generating function approach.Unlike the latter,it is,however,also applicable at high temperatures as it avoids harmonic and parallel-mode approximations and can be applied to study non-crystalline materials.The MD-ACF methodology thus promises to substantially widen the range of computational predictions of the optical properties of color centers and related defects.展开更多
This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics(GPUMD)package,GPUMD 4.0.We begin with a brief review of its development history,starting fr...This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics(GPUMD)package,GPUMD 4.0.We begin with a brief review of its development history,starting from the initial version.We then discuss the theoretical foundations for the development of the GPUMD package,including the formulations of the interatomic force,virial and heat current for many-body potentials,the development of the highly efficient and flexible neuroevolution potential(NEP)method,the supported integrators and related operations,the various physical properties that can be calculated on the fly,and the GPUMD ecosystem.After presenting these functionalities,we review a range of applications enabled by GPUMD,particularly in combination with the NEP approach.Finally,we outline possible future development directions for GPUMD.展开更多
基金funding from the Swedish Research Council(Nos.2020-04935 and 2021-05072)as well as computational resources provided by the National Academic Infrastructure for Supercomputing in Sweden at NSC,PDCC3SE partially funded by the Swedish Research Council through grant agreement No.2022-06725+1 种基金as well as the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at NSC.Parts of the computations were performed on resources provided by UNINETT Sigma2-the National Infrastructure for High-Performance Computing and Data Storage in Norway.C.L.acknowledges the support provided by the Research Council of Norway and the University of Oslo through the research project QuTe(no.325573,FriPro ToppForsk-program)funding from the Swedish Strategic Research Foundation through a Future Research Leader program(FFL21-0129).
文摘Color centers play key roles in,e.g.,solid state lighting and quantum information technology.Here,we describe an approach for predicting the optical line shapes of such emitters based on direct sampling of the underlying autocorrelation functions through molecular dynamics simulations(MD-ACF).The energy landscapes are represented by a machine-learned potential that describes both the ground and excited state landscapes through a single model,guaranteeing size-consistent predictions.We apply this methodology to the(V_(Si)V_(C))_(kk)^(0)divacancy defect in 4H-SiC and demonstrate that at low temperatures,the present MD-ACF approach reproduces results from the traditional generating function approach.Unlike the latter,it is,however,also applicable at high temperatures as it avoids harmonic and parallel-mode approximations and can be applied to study non-crystalline materials.The MD-ACF methodology thus promises to substantially widen the range of computational predictions of the optical properties of color centers and related defects.
基金supported by the National Science and Technology Advanced Materials Major Program of China(No.2024ZD0606900)W.Ouyang acknowledges the financial support from the National Natural Science Foundation of China(No.12472099)+10 种基金the Fundamental Research Funds for the Central Universities(No.2042025kf0050)S.Pan,Y.Wang,J.Shi,Z.Liang,J.Wang,and J.Sun acknowledge the financial support from the National Natural Science Foundation of China(Nos.12125404,T2495231,and 123B2049)the Basic Research Program of Jiangsu(Nos.BK20233001 and BK20241253)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Nos.2024ZB002 and 2024ZB075)the Postdoctoral Fellowship Program of CPSF(No.GZC20240695)the AI&AI for the Science Program of Nanjing University,and the Fundamental Research Funds for the Central Universities,as well as the computational resources provided by the High Performance Computing Center of Collaborative Innovation Center of Advanced Microstructures and the highperformance supercomputing center of Nanjing University.P.Guan acknowledges the financial support by the National Natural Science Foundation of China(No.T2325004)E.Lindgren,T.Hainer,L.Svensson.,J.Wiktor,E.Berger,and P.Erhart gratefully acknowledge funding from the Swedish Foundation for Strategic Research(GSn15-0008 and FFL21-0129)the Swedish Research Council(Nos.2020-04935 and 2021-05072)the Knut and Alice Wallenberg Foundation(Nos.2023.0032 and 2024.0042)the European Research Council(ERC Starting Grant No.101162195)as well as computational resources provided by the National Academic Infrastructure for Supercomputing in Sweden at NSC,PDC,and C3SE partially funded by the Swedish Research Council through grant agreement no.2022-06725,as well as the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at NSC.T.A-N.and Y.W.have been supported in part by the Academy of Finland through its QTF Center of Excellence program(project no.312298)European Union-NextGenerationEU instrument grant 353298.Computational resources by the CSC IT Center for Finland and the Aalto Science-IT are also gratefully acknowledged.
文摘This paper provides a comprehensive overview of the latest stable release of the graphics processing units molecular dynamics(GPUMD)package,GPUMD 4.0.We begin with a brief review of its development history,starting from the initial version.We then discuss the theoretical foundations for the development of the GPUMD package,including the formulations of the interatomic force,virial and heat current for many-body potentials,the development of the highly efficient and flexible neuroevolution potential(NEP)method,the supported integrators and related operations,the various physical properties that can be calculated on the fly,and the GPUMD ecosystem.After presenting these functionalities,we review a range of applications enabled by GPUMD,particularly in combination with the NEP approach.Finally,we outline possible future development directions for GPUMD.
