Ab initio modeling of dynamic structure factors(DSF)and related density response properties in the warm dense matter(WDM)regime is a challenging computational task.The DSF,convolved with a probing X-ray beam and instr...Ab initio modeling of dynamic structure factors(DSF)and related density response properties in the warm dense matter(WDM)regime is a challenging computational task.The DSF,convolved with a probing X-ray beam and instrument function,is measured in X-ray Thom-son scattering(XRTS)experiments,which allow the study of electronic structure properties at the microscopic level.Among the various ab initio methods,linear-response time-dependent density-functional theory(LR-TDDFT)is a key framework for simulating the DSF.The standard approach in LR-TDDFT for computing the DSF relies on the orbital representation.A significant drawback of this method is the unfavorable scaling of the number of required empty bands as the wavenumber increases,making LR-TDDFT impractical for modeling XRTS measurements over large energy scales,such as in backward scattering geometry.In this work,we consider and test an alternative approach to LR-TDDFT that employs the Liouville–Lanczos(LL)method for simulating the DSF of WDM.This approach does not require empty states and allows the DSF at large momentum transfer values and over a broad frequency range to be accessed.We compare the results obtained from the LL method with those from the solution of Dyson’s equation using the standard LR-TDDFT within the projector augmented-wave formalism for isochorically heated aluminum and warm dense hydrogen.Additionally,we utilize exact path integral Monte Carlo results for the imaginary-time density-density correlation function(ITCF)of warm dense hydrogen to rigorously benchmark the LL approach.We discuss the application of the LL method for calculating DSFs and ITCFs at different wavenumbers,the effects of pseudopotentials,and the role of Lorentzian smearing.The successful validation of the LL method under WDM conditions makes it a valuable addition to the ab initio simulation landscape,supporting experimental efforts and advancing WDM theory.展开更多
We present quasi-exact ab initio path integral Monte Carlo(PIMC)results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime,from solid density conditions to the...We present quasi-exact ab initio path integral Monte Carlo(PIMC)results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime,from solid density conditions to the strongly compressed case.The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both electronic and ionic exchange–correlation effects in the system,and to compare the results with those of earlier incomplete models such as the archetypal uniform electron gas or electrons in a fixed ion snapshot potential that do not take into account the interplay between the two constituents.The full electronic density response is highly sensitive to electronic localization around the ions,and our results constitute unambiguous predictions for upcoming X-ray Thomson scattering experiments with hydrogen jets and fusion plasmas.All PIMC results are made freely available and can be used directly for a gamut of applications,including inertial confinement fusion calculations and the modeling of dense astrophysical objects.Moreover,they constitute invaluable benchmark data for approximate but computationally less demanding approaches such as density functional theory or PIMC within the fixed-node approximation.展开更多
Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent.This fundamental tenet has remained experimentally challenging and is yet to be tested in the lab...Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent.This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory.A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence.This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime.Recently,the operation of the high-intensity Relativistic Laser at the X-ray Free Electron Laser provided by the Helmholtz International Beamline for Extreme Fields has been inaugurated at the High Energy Density scientific instrument of the European X-ray Free Electron Laser.We make the case that this worldwide unique combination of an X-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision X-ray polarimetry,refinements of prospective discovery scenarios and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity.展开更多
An accurate theoretical description of the dynamic properties of correlated quantum many-body systems,such as the dynamic structure factor S(q,ω),is important in many fields.Unfortunately,highly accurate quantum Mont...An accurate theoretical description of the dynamic properties of correlated quantum many-body systems,such as the dynamic structure factor S(q,ω),is important in many fields.Unfortunately,highly accurate quantum Monte Carlo methods are usually restricted to the imaginary time domain,and the analytic continuation of the imaginary-time density–density correlation function F(q,τ)to real frequencies is a notoriously hard problem.Here,it is argued that often no such analytic continuation is required because by definition,F(q,τ)contains the same physical information as does S(q,ω),only represented unfamiliarly.Specifically,it is shown how one can directly extract key information such as the temperature or quasi-particle excitation energies from theτdomain,which is highly relevant for equation-of-state measurements of matter under extreme conditions[T.Dornheim et al.,Nat.Commun.13,7911(2022)].As a practical example,ab initio path-integral Monte Carlo results for the uniform electron gas(UEG)are considered,and it is shown that even nontrivial processes such as the roton feature of the UEG at low density[T.Dornheim et al.,Commun.Phys.5,304(2022)]are manifested straightforwardly in F(q,τ).A comprehensive overview is given of various useful properties of F(q,τ)and how it relates to the usual dynamic structure factor.In fact,working directly in theτdomain is advantageous for many reasons and opens up multiple avenues for future applications.展开更多
The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior o...The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.展开更多
The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategi...The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.展开更多
Aquaculture and mariculture are becoming an increasingly important source of food supply in many countries and regions.However,with the expansion of aquaculture and mariculture comes increasing emissions of greenhouse...Aquaculture and mariculture are becoming an increasingly important source of food supply in many countries and regions.However,with the expansion of aquaculture and mariculture comes increasing emissions of greenhouse gases(GHG)which contribute to global warming and climate change.China leads the world in aquaculture and mariculture production,but there are no studies that systematically assess China's overall carbon footprint from these industries.This study quantified GHG emissions from aquaculture and mariculture by four source phases(feed,energy use,nitrous oxide and fertilizers),and then analyzed the carbon footprint of each of these phases for GHG production of nine major species groups over the past ten years to show the spatial distribution of GHG emissions from aquaculture and mariculture in China.Our results showed that the production of feed materials contributed most to the GHG emissions and found that crop energy use,crop land use changes(LUC),fertilizer production,crop nitrous oxide production and rice methane production were the main sources of feed emissions.The total GHG emissions of the nine species groups were 112 Mt(10^(9) kg)CO_(2)e,the nine species accounting for approximately 86%of aquaculture and mariculture production.GHG emissions of cyprinids had the highest contribution at 47%.Spatial analysis based on our study showed Guangdong,Hubei,Jiangsu and Shandong had the highest GHG emissions of all the provinces in this study,and they accounted for approximately 46%of all emissions.The regional Gross Domestic Product(GDP)was significantly positively correlated with GHG emissions in every province,with a correlation coefficient higher than 0.6.Our results showed for the first time the relationship between the relative production by species composition and spatial distribution of GHG emissions from aquaculture and mariculture in China.Our findings provide the scientific basis for reduction of GHG emissions within a broader context of expanding aquaculture in the future.展开更多
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials(ML-IAPs)for largescale spin-lattice dynamics simulations.The magneto-elastic ML-IAPs are constructed by couplin...A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials(ML-IAPs)for largescale spin-lattice dynamics simulations.The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP.Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed.Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations.We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP forα-iron.The combined potential energy surface yields excellent agreement with firstprinciples magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus,magnetization,and specific heat across the ferromagnetic–paramagnetic phase transition.展开更多
基金supported by the Center for Advanced Systems Understanding(CASUS),financed by Germany’s Federal Ministry of Education and Research(BMBF)and the Saxon State Government out of the State Budget approved by the Saxon State Parliamentfunding from the European Research Council(ERC)under the European Union’s Horizon 2022 research and innovation programme(Grant Agreement No.101076233,“PREXTREME”)funding from the European Union’s Just Transition Fund(JTF)within the project Röntgenlaser-Optimierung der Laserfusion(ROLF),Contract No.5086999001,co-financed by the Saxon State Government out of the State Budget approved by the Saxon State Parliament.
文摘Ab initio modeling of dynamic structure factors(DSF)and related density response properties in the warm dense matter(WDM)regime is a challenging computational task.The DSF,convolved with a probing X-ray beam and instrument function,is measured in X-ray Thom-son scattering(XRTS)experiments,which allow the study of electronic structure properties at the microscopic level.Among the various ab initio methods,linear-response time-dependent density-functional theory(LR-TDDFT)is a key framework for simulating the DSF.The standard approach in LR-TDDFT for computing the DSF relies on the orbital representation.A significant drawback of this method is the unfavorable scaling of the number of required empty bands as the wavenumber increases,making LR-TDDFT impractical for modeling XRTS measurements over large energy scales,such as in backward scattering geometry.In this work,we consider and test an alternative approach to LR-TDDFT that employs the Liouville–Lanczos(LL)method for simulating the DSF of WDM.This approach does not require empty states and allows the DSF at large momentum transfer values and over a broad frequency range to be accessed.We compare the results obtained from the LL method with those from the solution of Dyson’s equation using the standard LR-TDDFT within the projector augmented-wave formalism for isochorically heated aluminum and warm dense hydrogen.Additionally,we utilize exact path integral Monte Carlo results for the imaginary-time density-density correlation function(ITCF)of warm dense hydrogen to rigorously benchmark the LL approach.We discuss the application of the LL method for calculating DSFs and ITCFs at different wavenumbers,the effects of pseudopotentials,and the role of Lorentzian smearing.The successful validation of the LL method under WDM conditions makes it a valuable addition to the ab initio simulation landscape,supporting experimental efforts and advancing WDM theory.
基金supported by the Center for Advanced Systems Understanding(CASUS),financed by Germany’s Federal Ministry of Education and Research(BMBF)and the Saxon State Government out of the State Budget approved by the Saxon State Parliamentfunding from the European Research Council(ERC)under the European Union’s Horizon 2022 Research and Innovation Program(Grant Agreement No.101076233,“PREXTREME”).
文摘We present quasi-exact ab initio path integral Monte Carlo(PIMC)results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime,from solid density conditions to the strongly compressed case.The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both electronic and ionic exchange–correlation effects in the system,and to compare the results with those of earlier incomplete models such as the archetypal uniform electron gas or electrons in a fixed ion snapshot potential that do not take into account the interplay between the two constituents.The full electronic density response is highly sensitive to electronic localization around the ions,and our results constitute unambiguous predictions for upcoming X-ray Thomson scattering experiments with hydrogen jets and fusion plasmas.All PIMC results are made freely available and can be used directly for a gamut of applications,including inertial confinement fusion calculations and the modeling of dense astrophysical objects.Moreover,they constitute invaluable benchmark data for approximate but computationally less demanding approaches such as density functional theory or PIMC within the fixed-node approximation.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Grants Nos.392856280,416611371,416607684,416702141 and 416708866 within the Research Unit FOR2783/2 and Project-ID 278162697–SFB 1242.
文摘Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent.This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory.A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence.This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime.Recently,the operation of the high-intensity Relativistic Laser at the X-ray Free Electron Laser provided by the Helmholtz International Beamline for Extreme Fields has been inaugurated at the High Energy Density scientific instrument of the European X-ray Free Electron Laser.We make the case that this worldwide unique combination of an X-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision X-ray polarimetry,refinements of prospective discovery scenarios and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity.
基金supported partially by the Center for Advanced Systems Understanding(CASUS),which is financed by Germany’s Federal Ministry of Education and Research(BMBF),and by the state government of Saxony from the State budget approved by the Saxon State Parliament.This work has received funding from the European Research Council(ERC)under the European Union’s Horizon 2022 research and innovation program(Grant No.101076233,“PREXTREME”)The PIMC calculations were carried out at the Norddeutscher Verbund für Hoch-und Höchstleistungsrechnen(HLRN)under Grant No.shp00026,and on a Bull Cluster at the Center for Information Services and High Performance Computing(ZIH)at Technische Universität Dresden.
文摘An accurate theoretical description of the dynamic properties of correlated quantum many-body systems,such as the dynamic structure factor S(q,ω),is important in many fields.Unfortunately,highly accurate quantum Monte Carlo methods are usually restricted to the imaginary time domain,and the analytic continuation of the imaginary-time density–density correlation function F(q,τ)to real frequencies is a notoriously hard problem.Here,it is argued that often no such analytic continuation is required because by definition,F(q,τ)contains the same physical information as does S(q,ω),only represented unfamiliarly.Specifically,it is shown how one can directly extract key information such as the temperature or quasi-particle excitation energies from theτdomain,which is highly relevant for equation-of-state measurements of matter under extreme conditions[T.Dornheim et al.,Nat.Commun.13,7911(2022)].As a practical example,ab initio path-integral Monte Carlo results for the uniform electron gas(UEG)are considered,and it is shown that even nontrivial processes such as the roton feature of the UEG at low density[T.Dornheim et al.,Commun.Phys.5,304(2022)]are manifested straightforwardly in F(q,τ).A comprehensive overview is given of various useful properties of F(q,τ)and how it relates to the usual dynamic structure factor.In fact,working directly in theτdomain is advantageous for many reasons and opens up multiple avenues for future applications.
基金This work was in part supported by the Center for Advanced Systems Understanding(CASUS)which is financed by Germany’s Federal Ministry of Education and Research(BMBF)and by the Saxon state government out of the State budget approved by the Saxon State Parliament.
文摘The properties of electrons in matter are of fundamental importance.They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets.Modeling and simulation of such diverse applications rely primarily on density functional theory(DFT),which has become the principal method for predicting the electronic structure of matter.While DFT calculations have proven to be very useful,their computational scaling limits them to small systems.We have developed a machine learning framework for predicting the electronic structure on any length scale.It shows up to three orders of magnitude speedup on systems where DFT is tractable and,more importantly,enables predictions on scales where DFT calculations are infeasible.Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.
基金funded by the Center of Advanced Systems Understanding(CASUS)which is financed by Germany's Federal Ministry of Education and Research(BMBF)by the Saxon Ministry for Science,Culture and Tourism(SMWK)with tax funds on the basis of the budget approved by the Saxon State Parliament.
文摘The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.
基金supported by Basic and Applied Basic Research Foundation of Guangdong Province,China(No.2019B1515120065)the National Key R&D Program of China(Grant No.2018YFD0900904)+2 种基金INTERNATIONAL COOPERATION Project of the Chinese Academy of Sciences(Grant No.152342KYSB20190025)the National Natural Science Foundation of China of China(Grant No.31872687)This work was also partially funded by the Center of Advanced Systems Understanding(CASUS),which is financed by Germany's Federal Ministry of Education and Research(BMBF)and by the Saxon Ministry for Science,Culture and Tourism(SMWK)within the budget approved by the Saxon State Parliament.
文摘Aquaculture and mariculture are becoming an increasingly important source of food supply in many countries and regions.However,with the expansion of aquaculture and mariculture comes increasing emissions of greenhouse gases(GHG)which contribute to global warming and climate change.China leads the world in aquaculture and mariculture production,but there are no studies that systematically assess China's overall carbon footprint from these industries.This study quantified GHG emissions from aquaculture and mariculture by four source phases(feed,energy use,nitrous oxide and fertilizers),and then analyzed the carbon footprint of each of these phases for GHG production of nine major species groups over the past ten years to show the spatial distribution of GHG emissions from aquaculture and mariculture in China.Our results showed that the production of feed materials contributed most to the GHG emissions and found that crop energy use,crop land use changes(LUC),fertilizer production,crop nitrous oxide production and rice methane production were the main sources of feed emissions.The total GHG emissions of the nine species groups were 112 Mt(10^(9) kg)CO_(2)e,the nine species accounting for approximately 86%of aquaculture and mariculture production.GHG emissions of cyprinids had the highest contribution at 47%.Spatial analysis based on our study showed Guangdong,Hubei,Jiangsu and Shandong had the highest GHG emissions of all the provinces in this study,and they accounted for approximately 46%of all emissions.The regional Gross Domestic Product(GDP)was significantly positively correlated with GHG emissions in every province,with a correlation coefficient higher than 0.6.Our results showed for the first time the relationship between the relative production by species composition and spatial distribution of GHG emissions from aquaculture and mariculture in China.Our findings provide the scientific basis for reduction of GHG emissions within a broader context of expanding aquaculture in the future.
基金All authors thank Mark Wilson for his detailed review and edits.Sandia National Laboratories is a multimission laboratory managed and operated by National Technology&Engineering Solutions of Sandia,LLC,a wholly owned subsidiary of Honeywell International Inc.,for the U.S.Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.This paper describes objective technical results and analysis.Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S.Department of Energy or the United States Government.A.C.acknowledges funding from the Center for Advanced Systems Understanding(CASUS)which is financed by the German Federal Ministry of Education and Research(BMBF)and by the Saxon State Ministry for Science,Art,and Tourism(SMWK)with tax funds on the basis of the budget approved by the Saxon State Parliament.
文摘A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials(ML-IAPs)for largescale spin-lattice dynamics simulations.The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP.Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed.Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations.We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP forα-iron.The combined potential energy surface yields excellent agreement with firstprinciples magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus,magnetization,and specific heat across the ferromagnetic–paramagnetic phase transition.
