The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this wo...The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this work.The following properties were determined:density,self-diffusion coefficient,excess molar volume,and radial distribution function.The results show that with an increase in the mole fraction of[HPy][BF_(4)],the self-diffusion coefficient decreases.Additionally,the excess molar volume initially decreases,reaches a minimum,and then increases.The rules of radial distribution functions(RDFs)of characteristic atoms are different.With increasing the mole fraction of[HPy][BF_(4)],the first peak of the RDFs of HA1-F decreases,while that of CT6-CT6 rises at first and then decreases.This indicates that the solvent molecules affect the polar and non-polar regions of[HPy][BF_(4)]differently.展开更多
As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular...As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.展开更多
The effects of tensile temperatures ranging from 100 K to 900 K on the phase transition of hexagonal close-packed(HCP)zirconium were investigated by molecular dynamics simulations,which were combined with experimental...The effects of tensile temperatures ranging from 100 K to 900 K on the phase transition of hexagonal close-packed(HCP)zirconium were investigated by molecular dynamics simulations,which were combined with experimental observation under high resolution transmission electron microscopy.The results show that externally applied loading first induced the HCP to body-centered cubic(BCC)phase transition in the Pitsch-Schrader(PS)orientation relationship(OR).Then,the face-centered cubic(FCC)structure transformed from the BCC phase in the Bain path.However,the HCP-to-BCC transition was incomplete at 100 K and 300 K,resulting in a prismatic-type OR between the FCC and original HCP phase.Additionally,at the temperature ranging from 100 K to 600 K,the inverse BCC-to-HCP transition occurred locally following other variants of the PS OR,resulting in a basal-type relation between the newly generated HCP and FCC phases.A higher tensile temperature promoted the amount of FCC phase transforming into the BCC phase when the strain exceeded 45%.Besides,the crystal stretched at lower temperatures exhibits relatively higher strength but by the compromise of plasticity.This study reveals the deformation mechanisms in HCP-Zr at different temperatures,which may provide a better understanding of the deformation mechanism of zirconium alloys under different application environments.展开更多
文摘The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this work.The following properties were determined:density,self-diffusion coefficient,excess molar volume,and radial distribution function.The results show that with an increase in the mole fraction of[HPy][BF_(4)],the self-diffusion coefficient decreases.Additionally,the excess molar volume initially decreases,reaches a minimum,and then increases.The rules of radial distribution functions(RDFs)of characteristic atoms are different.With increasing the mole fraction of[HPy][BF_(4)],the first peak of the RDFs of HA1-F decreases,while that of CT6-CT6 rises at first and then decreases.This indicates that the solvent molecules affect the polar and non-polar regions of[HPy][BF_(4)]differently.
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(22325304,22221003 and 22033007)We acknowledge the Supercomputing Center of USTC,Hefei Advanced Computing Center,Beijing PARATERA Tech Co.,Ltd.,for providing high-performance computing services。
文摘As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.
基金Projects(51901248,51828102)supported by the National Natural Science Foundation of ChinaProject(2018JJ3649)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2019CX026)supported by the Innovation-driven Plan in Central South University,China。
文摘The effects of tensile temperatures ranging from 100 K to 900 K on the phase transition of hexagonal close-packed(HCP)zirconium were investigated by molecular dynamics simulations,which were combined with experimental observation under high resolution transmission electron microscopy.The results show that externally applied loading first induced the HCP to body-centered cubic(BCC)phase transition in the Pitsch-Schrader(PS)orientation relationship(OR).Then,the face-centered cubic(FCC)structure transformed from the BCC phase in the Bain path.However,the HCP-to-BCC transition was incomplete at 100 K and 300 K,resulting in a prismatic-type OR between the FCC and original HCP phase.Additionally,at the temperature ranging from 100 K to 600 K,the inverse BCC-to-HCP transition occurred locally following other variants of the PS OR,resulting in a basal-type relation between the newly generated HCP and FCC phases.A higher tensile temperature promoted the amount of FCC phase transforming into the BCC phase when the strain exceeded 45%.Besides,the crystal stretched at lower temperatures exhibits relatively higher strength but by the compromise of plasticity.This study reveals the deformation mechanisms in HCP-Zr at different temperatures,which may provide a better understanding of the deformation mechanism of zirconium alloys under different application environments.
