The optical properties of α-BeH2 in an Orthorhombic crystal structure with the space group (Ibam) are investigated. We have calculated the optical properties including dielelectric function, refractive index and exti...The optical properties of α-BeH2 in an Orthorhombic crystal structure with the space group (Ibam) are investigated. We have calculated the optical properties including dielelectric function, refractive index and extinction coefficient, using density functional approach. A theoretical explanation of the relationship between the dielectric function and other optical constants has been provided. Furthermore, the real and imaginary components of the dielectric function have been examined. The effects of the exchange-correlation potentials (GGA and GGA + U) applied on this compound’s absorption peaks and edges have also been investigated. It was found that using the GGA + U approximation caused the conduction band to shift, which in turn caused the initial absorption peak to shift.展开更多
The lattice parameter, bulk modulus and its pressure derivative of the wurtzite-type aluminium nitride （w-AlN） are investigated by using the Cambridge Serial Total Energy Package （CASTEP） program in the framework ...The lattice parameter, bulk modulus and its pressure derivative of the wurtzite-type aluminium nitride （w-AlN） are investigated by using the Cambridge Serial Total Energy Package （CASTEP） program in the framework of Density Functional Theory （DFT）. The calculated results are in good agreement with the available experimental data and other theoretical results. Through the quasi-harmonic Debye model, the dependences of the normalized lattice parameters a/ao and c/c0, axial ratio c/a, normalized primitive-cell volume V/Vo, Debye temperature θD and heat capacity Cv on pressure P and temperature T are obtained. It is found that the interlayer covalent interactions （Al-N bonds） are more （even a little） sensitive to temperature and pressure than intralayer ones （N-N bonds）, which gives rise to a little lattice anisotropy in the w-AlN.展开更多
Machine learning potentials(MLPs)have become an indispensable tool in large-scale atomistic simulations.However,mostMLPs today are trained on data computed using relatively cheap density functional theory(DFT)methods ...Machine learning potentials(MLPs)have become an indispensable tool in large-scale atomistic simulations.However,mostMLPs today are trained on data computed using relatively cheap density functional theory(DFT)methods such as the Perdew-Burke-Ernzerhof(PBE)generalized gradient approximation(GGA)functional.While meta-GGAs such as the strongly constrained and appropriately normed(SCAN)functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems,their higher computational cost remains an impediment to their use in MLP development.In this work,we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network(M3GNet)interatomic potentials that integrate different levels of theory within a singlemodel.Using silicon and water as examples,we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelityGGAcalculations with 10%of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8×the number of SCAN calculations.This work provides a pathway to the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets.展开更多
文摘The optical properties of α-BeH2 in an Orthorhombic crystal structure with the space group (Ibam) are investigated. We have calculated the optical properties including dielelectric function, refractive index and extinction coefficient, using density functional approach. A theoretical explanation of the relationship between the dielectric function and other optical constants has been provided. Furthermore, the real and imaginary components of the dielectric function have been examined. The effects of the exchange-correlation potentials (GGA and GGA + U) applied on this compound’s absorption peaks and edges have also been investigated. It was found that using the GGA + U approximation caused the conduction band to shift, which in turn caused the initial absorption peak to shift.
基金Project supported by the National Natural Science Foundation of China （Grant No 10576020）.
文摘The lattice parameter, bulk modulus and its pressure derivative of the wurtzite-type aluminium nitride （w-AlN） are investigated by using the Cambridge Serial Total Energy Package （CASTEP） program in the framework of Density Functional Theory （DFT）. The calculated results are in good agreement with the available experimental data and other theoretical results. Through the quasi-harmonic Debye model, the dependences of the normalized lattice parameters a/ao and c/c0, axial ratio c/a, normalized primitive-cell volume V/Vo, Debye temperature θD and heat capacity Cv on pressure P and temperature T are obtained. It is found that the interlayer covalent interactions （Al-N bonds） are more （even a little） sensitive to temperature and pressure than intralayer ones （N-N bonds）, which gives rise to a little lattice anisotropy in the w-AlN.
基金ntellectually led by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract No. DE-AC02-05-CH11231 (Materials Project program KC23MP)This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DOE-ERCAP0026371the support of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures program.
文摘Machine learning potentials(MLPs)have become an indispensable tool in large-scale atomistic simulations.However,mostMLPs today are trained on data computed using relatively cheap density functional theory(DFT)methods such as the Perdew-Burke-Ernzerhof(PBE)generalized gradient approximation(GGA)functional.While meta-GGAs such as the strongly constrained and appropriately normed(SCAN)functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems,their higher computational cost remains an impediment to their use in MLP development.In this work,we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network(M3GNet)interatomic potentials that integrate different levels of theory within a singlemodel.Using silicon and water as examples,we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelityGGAcalculations with 10%of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8×the number of SCAN calculations.This work provides a pathway to the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets.
