A series of 2D direct numerical simulations were performed with an accurate level set method for single drop impacts.The adopted ACLS method was validated to be efficient with perfect mass conservation in both normal ...A series of 2D direct numerical simulations were performed with an accurate level set method for single drop impacts.The adopted ACLS method was validated to be efficient with perfect mass conservation in both normal and oblique impacts.A square-root correction for neck bases was modified in accuracy as well as scope of applications.In addition,process of jet formation and evolution was studied to reveal internal dynamics in drop impacts.It's found that pressure gradient and vortex are coexisting and completive reasons for jet topology while the inclined angle has a significant effect on them.Mechanisms of jet formation and evolution are different in the front and back necks.With the help of PDF distribution and correction calculation,a compromise in the competition is observed.This work lays a solid foundation for further studies of dynamics in gas-liquid flows.展开更多
Artificial neural network(ANN)potentials enable accurate atomistic simulations of complex materials at unprecedented scales,but training them for potential energy surfaces(PES)of diverse chemical environments remains ...Artificial neural network(ANN)potentials enable accurate atomistic simulations of complex materials at unprecedented scales,but training them for potential energy surfaces(PES)of diverse chemical environments remains computationally intensive,especially when the PES gradients are trained on atomic force data.Here,we present an efficient methodology incorporating forces intoANNtraining by translating them to synthetic energy data using Gaussian process regression(GPR),leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training.We evaluated the method on hybrid density-functional theory data for ethylene carbonate(EC)molecules and their interfaces with Li metal,which are relevant for Li-metal batteries.The GPR-ANN potentials achieved an accuracy comparable to fully force-trained ANN potentials with a significantly reduced computational and memory overhead,establishing the method as a powerful and scalable framework for constructing high-fidelity ANN potentials for complex materials systems.展开更多
基金Supported by the National Natural Science Foundation of China(91541202,51276163)
文摘A series of 2D direct numerical simulations were performed with an accurate level set method for single drop impacts.The adopted ACLS method was validated to be efficient with perfect mass conservation in both normal and oblique impacts.A square-root correction for neck bases was modified in accuracy as well as scope of applications.In addition,process of jet formation and evolution was studied to reveal internal dynamics in drop impacts.It's found that pressure gradient and vortex are coexisting and completive reasons for jet topology while the inclined angle has a significant effect on them.Mechanisms of jet formation and evolution are different in the front and back necks.With the help of PDF distribution and correction calculation,a compromise in the competition is observed.This work lays a solid foundation for further studies of dynamics in gas-liquid flows.
基金support by the Columbia Center for Computational Electrochemistry(CCCE)and computing resources from Columbia University’s Shared Research Computing FacilityJ.L.Z.and N.A.thank the Project HPC-EUROPA3(Grant No.INFRAIA-2016-1-730897)for its support,provided through the EC Research and Innovation Action under the H2020 ProgramN.A.also acknowledges support by a start-up grant(Dutch Sector Plan)from Utrecht University.The authors gratefully acknowledge discussions with JoséA.Garrido Torres and Thomas Bligaard.
文摘Artificial neural network(ANN)potentials enable accurate atomistic simulations of complex materials at unprecedented scales,but training them for potential energy surfaces(PES)of diverse chemical environments remains computationally intensive,especially when the PES gradients are trained on atomic force data.Here,we present an efficient methodology incorporating forces intoANNtraining by translating them to synthetic energy data using Gaussian process regression(GPR),leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training.We evaluated the method on hybrid density-functional theory data for ethylene carbonate(EC)molecules and their interfaces with Li metal,which are relevant for Li-metal batteries.The GPR-ANN potentials achieved an accuracy comparable to fully force-trained ANN potentials with a significantly reduced computational and memory overhead,establishing the method as a powerful and scalable framework for constructing high-fidelity ANN potentials for complex materials systems.
