By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pr...By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pressure P=7.9×10^(3)-1.6×10^(6) GPa and temperature T=25-2800 eV),silicon(P=2.6×10^(3)-7.9×10^(5) GPa and T=21.5-1393 eV),and aluminum(P=5.2×10^(3)-9.0×10^(5) GPa and T=25-1393 eV)over wide ranges of pressure and temperature.In particular,we systematically investigate the impact of different cutoff radii in norm-conserving pseudopotentials on the calculated properties at elevated temperatures,such as pressure,ionization energy,and equation of state.By comparing the SDFT and MDFT results with those of other first-principles methods,such as extended first-principles molecular dynamics and path integral Monte Carlo methods,we find that the SDFT and MDFT methods show satisfactory precision,which advances our understanding of first-principles methods when applied to studies of matter at extremely high pressures and temperatures.展开更多
In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at ...In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures.However,stochastic density functional theory(SDFT)can overcome this limitation.Recently,SDFT and the related mixed stochastic–deterministic density functional theory,based on a plane-wave basis set,have been implemented in the first-principles electronic structure software ABACUS[Q.Liu and M.Chen,Phys.Rev.B 106,125132(2022)].In this study,we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV.Importantly,we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories.Subsequently,we compute and analyze the structural properties,dynamic properties,and transport coefficients of warm dense matter.展开更多
We propose an efficient scheme that combines density functional theory(DFT)with deep potentials(DPs),to systematically study convergence issues in the computation of the electronic thermal conductivity of warm dense a...We propose an efficient scheme that combines density functional theory(DFT)with deep potentials(DPs),to systematically study convergence issues in the computation of the electronic thermal conductivity of warm dense aluminum(2.7 g/cm^(3)and temperatures ranging from 0.5 eV to 5.0 eV)with respect to the number of k-points,the number of atoms,the broadening parameter,the exchange-correlation functionals,and the pseudopotentials.Furthermore,we obtain the ionic thermal conductivity using the Green–Kubo method in conjunction with DP molecular dynamics simulations,and we study size effects on the ionic thermal conductivity.This work demonstrates that the proposed method is efficient in evaluating both electronic and ionic thermal conductivities of materials.展开更多
The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and design.AI-driven potential energy models havedemonstrated the capability to conduct large-sc...The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and design.AI-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure methods.However,the model generation process remains a bottleneck for large-scale applications.We propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular modeling.In this study,we introduce the DPA-2 architecture as a prototype for LAMs.Pre-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies.Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.展开更多
Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpos...Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”bottlenecks.Consequently,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and duration.To solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio accuracy.With significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.展开更多
In the information-explosion era,developing novel algorithms and memristive devices has become a promising concept for next-generation capacity enlargement technology.Organic small molecule-based devices displaying su...In the information-explosion era,developing novel algorithms and memristive devices has become a promising concept for next-generation capacity enlargement technology.Organic small molecule-based devices displaying superior learning-memory performance have attracted much attention,except for the existence of poor heat-resilience and mediocre conductivity.In this paper,a strategy of transforming an organic-type data-storage material to metal complex is proposed to resolve these intrinsic issues.A pristine NDI-derivative(NIPy)and its corresponding Co(II)complex(CoNIPy)are synthesized for the purpose of electrical property investigation.CoNIPy complex-based memristive device exhibits superior ternary WORM memory performance compared with the binary behavior of NIPy,including>104 s of reading,lower threshold voltage(V_(th)),1:10^(2):10^(5)of OFF/ON1/ON2 current ratio,and long-term stability in heating environment.The variable learning-memory behavior can be attributed to the enhanced ligand-to-metal charge transfer(LMCT)and improved redox activity after the introduction of central metal atom and coordination bond.These studies on material innovation and optimal performance are of great importance not only for environmentally-robust memristive devices but also for practical application of a host of organic electronic devices.展开更多
Based on ab initio molecular dynamics simulations and density functional theory, we performed a systematic theoretical study to elucidate the correlation between the H-bonded environment and X- ray emission spectra of...Based on ab initio molecular dynamics simulations and density functional theory, we performed a systematic theoretical study to elucidate the correlation between the H-bonded environment and X- ray emission spectra of liquid water. The spectra generated from excited water molecules embedded in an intact H-bonded environment yield broader spectral peaks and a larger spectral range than the spectra generated from water molecules in a broken H-bonded environment. Such differences are caused by the local electronic structures on the excited water molecules within the core-hole lifetime that evolve differently through the rearrangement of neighboring water molecules in different H-bonded environments.展开更多
基金supported by the National Key R&D Program of China under Grant No.2025YFB3003603the National Natural Science Foundation of China under Grant Nos.12135002 and 12105209.
文摘By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pressure P=7.9×10^(3)-1.6×10^(6) GPa and temperature T=25-2800 eV),silicon(P=2.6×10^(3)-7.9×10^(5) GPa and T=21.5-1393 eV),and aluminum(P=5.2×10^(3)-9.0×10^(5) GPa and T=25-1393 eV)over wide ranges of pressure and temperature.In particular,we systematically investigate the impact of different cutoff radii in norm-conserving pseudopotentials on the calculated properties at elevated temperatures,such as pressure,ionization energy,and equation of state.By comparing the SDFT and MDFT results with those of other first-principles methods,such as extended first-principles molecular dynamics and path integral Monte Carlo methods,we find that the SDFT and MDFT methods show satisfactory precision,which advances our understanding of first-principles methods when applied to studies of matter at extremely high pressures and temperatures.
基金supported by the National Natural Science Foundation of China under Grant Nos.12122401 and 12074007.
文摘In traditional finite-temperature Kohn–Sham density functional theory(KSDFT),the partial occupation of a large number of high-energy KS eigenstates restricts the use of first-principles molecular dynamics methods at extremely high temperatures.However,stochastic density functional theory(SDFT)can overcome this limitation.Recently,SDFT and the related mixed stochastic–deterministic density functional theory,based on a plane-wave basis set,have been implemented in the first-principles electronic structure software ABACUS[Q.Liu and M.Chen,Phys.Rev.B 106,125132(2022)].In this study,we combine SDFT with the Born–Oppenheimer molecular dynamics method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV.Importantly,we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories.Subsequently,we compute and analyze the structural properties,dynamic properties,and transport coefficients of warm dense matter.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDC01040100M.C.is supported by the National Science Foundation of China under Grant No.12074007.The numerical simulations were performed on the High Performance Computing Platform of CAPT.
文摘We propose an efficient scheme that combines density functional theory(DFT)with deep potentials(DPs),to systematically study convergence issues in the computation of the electronic thermal conductivity of warm dense aluminum(2.7 g/cm^(3)and temperatures ranging from 0.5 eV to 5.0 eV)with respect to the number of k-points,the number of atoms,the broadening parameter,the exchange-correlation functionals,and the pseudopotentials.Furthermore,we obtain the ionic thermal conductivity using the Green–Kubo method in conjunction with DP molecular dynamics simulations,and we study size effects on the ionic thermal conductivity.This work demonstrates that the proposed method is efficient in evaluating both electronic and ionic thermal conductivities of materials.
基金supported by the National Key R&D Program of China(grantno.2022YFA1004300)the National Natural Science Foundation of China(grant no.12122103)+11 种基金supported by the National Key Research and Development Project of China(grant no.2022YFA1004302)the National Natural Science Foundation of China(grants nos.92270001 and 12288101)supported by the National Institutes of Health(grant no.GM107485 to D.M.Y.)the National Science Foundation(grant no.2209718 to D.M.Y.)supported by the Natural Science Foundation of Zhejiang Province(grant no.2022XHSJJ006)supported by the National Natural Science Foundation of China(grants nos.22222303 and 22173032)supported by the National Key R&D Program of China(grants nos.2021YFA0718900 and 2022YFA1403000)supported by the National Natural Science Foundation of China(grants nos.12034009 and 91961204)supported by the National Science Fund for Distinguished Young Scholars(grant no.22225302)Laboratory of AI for Electrochemistry(AI4EC),and IKKEM(grants nos.RD2023100101 and RD2022070501)supported by the National Natural Science Foundation of China(grants nos.12122401,12074007,and 12135002)Lastly,the computational resource was supported by the Bohrium Cloud Platform at DP Technology and Tan Kah Kee Supercomputing Center(IKKEM).
文摘The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and design.AI-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure methods.However,the model generation process remains a bottleneck for large-scale applications.We propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular modeling.In this study,we introduce the DPA-2 architecture as a prototype for LAMs.Pre-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies.Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.
基金supported by the National Natural Science Foundation of China(62474058 and 61804049)the Yuelushan Center for Industrial Innovation(2023YCII0104)+2 种基金the Huxiang High Level Talent Gathering Project(2019RS1023)the Technology Innovation and Entrepreneurship Funds of Hunan Province,P.R.China(2019GK5029)the Fund for Distinguished Young Scholars of Changsha(kq1905012).
文摘Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”bottlenecks.Consequently,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and duration.To solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio accuracy.With significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.
基金Y.L.thanks financial support from the National Natural Science Foundation of China(Grants No.22008164)the Natural Science Foundation of Jiangsu Province(Grants No.BK20190939)+4 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grants No.19KJB150018)This work is also supported by Six Talent Peaks Project of Jiangsu Province,China(XCL-078)Jiangsu Key Disciplines of the Fourteenth Five-Year Plan(2021135)the Suzhou Key Laboratory for Low Dimensional Optoelectronic Materials and Devices(SzS201611)Q.Z.thanks thefunding support from City University of Hong Kong(9380117,7005620 and 7020040)and Hong Kong Institute for Advanced Study,City University of Hong Kong,Hong Kong,P.R.China.
文摘In the information-explosion era,developing novel algorithms and memristive devices has become a promising concept for next-generation capacity enlargement technology.Organic small molecule-based devices displaying superior learning-memory performance have attracted much attention,except for the existence of poor heat-resilience and mediocre conductivity.In this paper,a strategy of transforming an organic-type data-storage material to metal complex is proposed to resolve these intrinsic issues.A pristine NDI-derivative(NIPy)and its corresponding Co(II)complex(CoNIPy)are synthesized for the purpose of electrical property investigation.CoNIPy complex-based memristive device exhibits superior ternary WORM memory performance compared with the binary behavior of NIPy,including>104 s of reading,lower threshold voltage(V_(th)),1:10^(2):10^(5)of OFF/ON1/ON2 current ratio,and long-term stability in heating environment.The variable learning-memory behavior can be attributed to the enhanced ligand-to-metal charge transfer(LMCT)and improved redox activity after the introduction of central metal atom and coordination bond.These studies on material innovation and optimal performance are of great importance not only for environmentally-robust memristive devices but also for practical application of a host of organic electronic devices.
文摘Based on ab initio molecular dynamics simulations and density functional theory, we performed a systematic theoretical study to elucidate the correlation between the H-bonded environment and X- ray emission spectra of liquid water. The spectra generated from excited water molecules embedded in an intact H-bonded environment yield broader spectral peaks and a larger spectral range than the spectra generated from water molecules in a broken H-bonded environment. Such differences are caused by the local electronic structures on the excited water molecules within the core-hole lifetime that evolve differently through the rearrangement of neighboring water molecules in different H-bonded environments.
