Distributed computing frameworks are the fundamental component of distributed computing systems.They provide an essential way to support the efficient processing of big data on clusters or cloud.The size of big data i...Distributed computing frameworks are the fundamental component of distributed computing systems.They provide an essential way to support the efficient processing of big data on clusters or cloud.The size of big data increases at a pace that is faster than the increase in the big data processing capacity of clusters.Thus,distributed computing frameworks based on the MapReduce computing model are not adequate to support big data analysis tasks which often require running complex analytical algorithms on extremely big data sets in terabytes.In performing such tasks,these frameworks face three challenges:computational inefficiency due to high I/O and communication costs,non-scalability to big data due to memory limit,and limited analytical algorithms because many serial algorithms cannot be implemented in the MapReduce programming model.New distributed computing frameworks need to be developed to conquer these challenges.In this paper,we review MapReduce-type distributed computing frameworks that are currently used in handling big data and discuss their problems when conducting big data analysis.In addition,we present a non-MapReduce distributed computing framework that has the potential to overcome big data analysis challenges.展开更多
Background Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation.Joint calling is routinely used to combine identified variants across ...Background Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation.Joint calling is routinely used to combine identified variants across multiple related samples.However,the improvement of variants identification using the mutual support information from mul-tiple samples remains quite limited for population-scale genotyping.Results In this study,we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples’data.The variants were accurately identified from multiple samples by using four steps:(1)Probabilities of variants from two widely used algorithms,GATK and Freebayes,were calculated by Poisson model incorporating base sequencing error potential;(2)The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification(rHID)variants database;(3)The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate(FDR)using rHID database;(4)To avoid the elimination of potentially true variants from rHID database,the vari-ants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants.The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32%compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number(GPC5),scrapie pathology(PAPSS2),sea-sonal reproduction and litter size(GRM1),coat color(RAB27A),and lentivirus susceptibility(TMEM154).Conclusion The new method used the computational strategy to reduce the number of false positives,and simulta-neously improve the identification of genetic variants.This strategy did not incur any extra cost by using any addi-tional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.展开更多
CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identif...CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif(PAM)sequences.Due to the limitations of experimental methods,bioinformatics approaches have become essential.However,existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems,resulting in low accuracy.To address this,we develop PAMPHLET,a pipeline that uses homology searches to identify additional spacers,significantly increasing the number of spacers up to 18-fold.PAMPHLET is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 protospacers.These predictions are further validated using the DocMF platform,which characterizes protein-DNA recognition patterns via next-generation sequencing.The high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy,expedite the discovery process,and accelerate the development of CRISPR tools.展开更多
Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The ...Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The graph structure is a typical tool used to formulate such correlations,it is incapable of modeling highorder correlations among different objects in systems;thus,the graph structure cannot fully convey the intricate correlations among objects.Confronted with the aforementioned two challenges,hypergraph computation models high-order correlations among data,knowledge,and rules through hyperedges and leverages these high-order correlations to enhance the data.Additionally,hypergraph computation achieves collaborative computation using data and high-order correlations,thereby offering greater modeling flexibility.In particular,we introduce three types of hypergraph computation methods:①hypergraph structure modeling,②hypergraph semantic computing,and③efficient hypergraph computing.We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional(3D)object recognition,revealing that hypergraph computation can reduce the data requirement by 80%while achieving comparable performance or improve the performance by 52%given the same data,compared with a traditional data-based method.A comprehensive overview of the applications of hypergraph computation in diverse domains,such as intelligent medicine and computer vision,is also provided.Finally,we introduce an open-source deep learning library,DeepHypergraph(DHG),which can serve as a tool for the practical usage of hypergraph computation.展开更多
Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence.Ab initio simulations have played a fundamental role in shaping our understanding of this proce...Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence.Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost.Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients.We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semito-full quantitative agreement with ab initio methods reducing the computational cost by about 80%.Moreover,we show that this framework naturally extends to molecular dynamics simulations,paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths.展开更多
Accurate first-principles prediction of lattice thermal conductivity(κ_(L))remains challenging in identifying materials with extreme thermal behavior.While the harmonic approximation with threephonon scattering(HA+3p...Accurate first-principles prediction of lattice thermal conductivity(κ_(L))remains challenging in identifying materials with extreme thermal behavior.While the harmonic approximation with threephonon scattering(HA+3ph)is now routine,reliableκ_(L)prediction often requires higher-order anharmonic effects,including self-consistent phonon renormalization,three-and four-phonon scattering,and off-diagonal heat flux(SCPH+3,4ph+OD).We present a state-of-the-art highthroughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures.From 562 dynamically stable compounds,weassess the hierarchical impacts of higher-order anharmonicity.For around 60%of materials,HA+3ph predictions closely match those from SCPH+3,4ph+OD.SCPH generally increasesκ_(L),by over 8 times in extreme cases,whereas four-phonon scattering universally suppressesκ_(L),sometimes to 15%of the HA+3ph value.Off-diagonal contributions are negligible in high-κ_(L)systems but can rival diagonal terms in highly anharmonic low-κ_(L)compounds.We highlight four case studies,Rb_(2)TlAlH_(6),Cu_(3)VSe_(4),CuBr,and KTlCl_(4),that exhibit distinct extreme behaviors.This work delivers not only a robust workflow for high-fidelityκ_(L)dataset but also a quantitative framework to determine when higher-order effects are essential.The hierarchy ofκ_(L)results,from the HA+3ph to SCPH+3,4ph+OD level,offers a scalable,interpretable route to discovering next-generation extreme thermal materials.展开更多
Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects.Fog is characterized by low latency with a ...Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects.Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based services.To provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities.Discrete EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular way.In this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing Framework.Simulation results show that this new approach significantly,improves energy efficiency of buildings and reduces latency.Additionally,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.展开更多
With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex structures.The introduction of streaming,temporal,multilayer,and hyper...With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex structures.The introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional requirements.For instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading processes.Unfortunately,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality updates.This results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion library.First,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework functionalities.Finally,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer networks.We conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.展开更多
This paper investigates mixed convection heat transfer in vertical multilayer flow in a system consisting of a viscous fluid flanked by nanofluids in a porous medium,taking account of magnetohydrodynamic(MHD)and radia...This paper investigates mixed convection heat transfer in vertical multilayer flow in a system consisting of a viscous fluid flanked by nanofluids in a porous medium,taking account of magnetohydrodynamic(MHD)and radiation effects and internal heat generation.The thermal conductivity of the nanofluids is analyzed using the Maxwell-Garnett and Patel models.A computational framework for solving the governing nonlinear differential equations using an analytical and perturbative approach is established,to provide accurate predictions of heat transfer characteristics.The interplay between the viscous fluid and the nanofluids in the presence of MHD effects introduces complex thermal and fluid dynamic interactions,highlighting the need for innovative modeling approaches.The results obtained provides enhanced understanding of multiphase flow behavior in the presence of internal heat generation and external magnetic fields.They will contribute to the development of methods for optimizing heat transfer in advanced thermal management applications such as nuclear reactor cooling,medical management of hyperthermia,and industrial energy systems.展开更多
Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stabil...Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges.Herein,we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts,using machine-learned force fields.We propose a new catalytic descriptor,termed adsorption energy distribution,that aggregates the binding energies for different catalyst facets,binding sites,and adsorbates.The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates.By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys,we offer a powerful tool for catalyst discovery.We propose new promising candidates such as ZnRh and ZnPt_(3),which to our knowledge,have not yet been tested,and discuss their possible advantage in terms of stability.展开更多
Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses chal...Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.展开更多
The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alter...The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alternative to first-principles simulations.This study presents EMFF-2025,a general NNP model for C,H,N,and O-based HEMs,leveraging transfer learning with minimal data from DFT calculations.The model achieves DFT-level accuracy,predicting the structure,mechanical properties,and decomposition characteristics of 20 HEMs.Integrating EMFF-2025 with PCA and correlation heatmaps,we map the chemical space and structural evolution of these HEMs across temperatures.Surprisingly,EMFF-2025 uncovers that most HEMs follow similar hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.展开更多
Accurate first-principles-based prediction of the pressure-composition-temperature(PCT)relationships of metal hydrides can enable predictive optimization of hydrogen capacities and pressures.In this work,we introduce ...Accurate first-principles-based prediction of the pressure-composition-temperature(PCT)relationships of metal hydrides can enable predictive optimization of hydrogen capacities and pressures.In this work,we introduce a novel computational framework that integrates density functional theory(DFT)with a Python-based PCT Simulation Toolkit to predict PCT diagrams with high accuracy.By using only structural input data from the metallic phase,this toolkit automates the detection of interstitial voids,generates input files for DFT calculations,and constructs thermodynamic models based on para-equilibrium principles.We validate this approach across five major metal-hydride classes–BCC and FCC alloys,AB_(5),AB_(2),and AB compounds-and demonstrate that even with minimal computational effort,key hydrogen sorption characteristics can be reliably determined.Using the PBE functional without vibrational contribution,our results show that hydrogen capacity predictions achieve a mean accuracy of 95%,while sorption pressures are modeled within one order of magnitude of experimental values.Furthermore,our method can implicitly account for the phase transition in metal hydrides and can reliably model multicomponent alloys with representative alloys of lesser chemical complexity.This framework enables rapid and accurate exploration of metal hydrides to design alloys for new applications.展开更多
Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales.Here,we develop an integrated computational f...Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales.Here,we develop an integrated computational framework combining on-lattice kinetic Monte Carlo(KMC)simulations,absorbing Markov chain models,and mesoscale cluster dynamics(CD)to investigate these interactions in Al-Mg-Zn alloys.The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy.These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching,which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging.Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics.Results provide insights to guide alloy chemistry,quench rates,and aging time at finite temperatures to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.展开更多
The three-dimensional discontinuous deformation analysis(3D-DDA) is a promising numerical method for both static and dynamic analyses of rock systems. Lacking mature software, its popularity is far behind its ability....The three-dimensional discontinuous deformation analysis(3D-DDA) is a promising numerical method for both static and dynamic analyses of rock systems. Lacking mature software, its popularity is far behind its ability. To address this problem, this paper presents a new software architecture from a software engineering viewpoint. Based on 3D-DDA characteristics, the implementation of the proposed architecture has the following merits. Firstly, the software architecture separates data, computing, visualization, and signal control into individual modules. Secondly, data storage and parallel access are fully considered for different conditions. Thirdly, an open computing framework is provided which supports most numerical computing methods; common tools for equation solving and parallel computing are provided for further development. Fourthly, efficient visualization functions are provided by integrating a variety of visualization algorithms. A user-friendly graphical user interface is designed to improve the user experience. Finally, through a set of examples, the software is verified against both analytical solutions and the original code by Dr. Shi Gen Hua.展开更多
基金supported by the National Natural Science Foundation of China(No.61972261)Basic Research Foundations of Shenzhen(Nos.JCYJ 20210324093609026 and JCYJ20200813091134001).
文摘Distributed computing frameworks are the fundamental component of distributed computing systems.They provide an essential way to support the efficient processing of big data on clusters or cloud.The size of big data increases at a pace that is faster than the increase in the big data processing capacity of clusters.Thus,distributed computing frameworks based on the MapReduce computing model are not adequate to support big data analysis tasks which often require running complex analytical algorithms on extremely big data sets in terabytes.In performing such tasks,these frameworks face three challenges:computational inefficiency due to high I/O and communication costs,non-scalability to big data due to memory limit,and limited analytical algorithms because many serial algorithms cannot be implemented in the MapReduce programming model.New distributed computing frameworks need to be developed to conquer these challenges.In this paper,we review MapReduce-type distributed computing frameworks that are currently used in handling big data and discuss their problems when conducting big data analysis.In addition,we present a non-MapReduce distributed computing framework that has the potential to overcome big data analysis challenges.
基金Superior Farms sheep producersIBEST for their supportfinancial support from the Idaho Global Entrepreneurial Mission
文摘Background Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation.Joint calling is routinely used to combine identified variants across multiple related samples.However,the improvement of variants identification using the mutual support information from mul-tiple samples remains quite limited for population-scale genotyping.Results In this study,we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples’data.The variants were accurately identified from multiple samples by using four steps:(1)Probabilities of variants from two widely used algorithms,GATK and Freebayes,were calculated by Poisson model incorporating base sequencing error potential;(2)The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification(rHID)variants database;(3)The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate(FDR)using rHID database;(4)To avoid the elimination of potentially true variants from rHID database,the vari-ants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants.The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32%compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number(GPC5),scrapie pathology(PAPSS2),sea-sonal reproduction and litter size(GRM1),coat color(RAB27A),and lentivirus susceptibility(TMEM154).Conclusion The new method used the computational strategy to reduce the number of false positives,and simulta-neously improve the identification of genetic variants.This strategy did not incur any extra cost by using any addi-tional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.
基金supported by grants from the Foundation for Distinguished Young Talents in Higher Education of Guangdong,China(2024KQNCX157)Our work was also supported in part by the Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College(2022B1212010006)+1 种基金in part by the Guangdong Higher Education Upgrading Plan(2021-2025)of“Rushing to the Top,Making Up Shortcomings and Strengthening Special Features”(R0400001-22)Additionally,we acknowledge support from the Zhuhai Basic and Applied Basic ResearchFoundation(2220004002717).
文摘CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif(PAM)sequences.Due to the limitations of experimental methods,bioinformatics approaches have become essential.However,existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems,resulting in low accuracy.To address this,we develop PAMPHLET,a pipeline that uses homology searches to identify additional spacers,significantly increasing the number of spacers up to 18-fold.PAMPHLET is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 protospacers.These predictions are further validated using the DocMF platform,which characterizes protein-DNA recognition patterns via next-generation sequencing.The high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy,expedite the discovery process,and accelerate the development of CRISPR tools.
文摘Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The graph structure is a typical tool used to formulate such correlations,it is incapable of modeling highorder correlations among different objects in systems;thus,the graph structure cannot fully convey the intricate correlations among objects.Confronted with the aforementioned two challenges,hypergraph computation models high-order correlations among data,knowledge,and rules through hyperedges and leverages these high-order correlations to enhance the data.Additionally,hypergraph computation achieves collaborative computation using data and high-order correlations,thereby offering greater modeling flexibility.In particular,we introduce three types of hypergraph computation methods:①hypergraph structure modeling,②hypergraph semantic computing,and③efficient hypergraph computing.We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional(3D)object recognition,revealing that hypergraph computation can reduce the data requirement by 80%while achieving comparable performance or improve the performance by 52%given the same data,compared with a traditional data-based method.A comprehensive overview of the applications of hypergraph computation in diverse domains,such as intelligent medicine and computer vision,is also provided.Finally,we introduce an open-source deep learning library,DeepHypergraph(DHG),which can serve as a tool for the practical usage of hypergraph computation.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No.[948493])Computational resources were provided by the Trinity College Research IT and the Irish Centre for High-End Computing(ICHEC).
文摘Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence.Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost.Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients.We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semito-full quantitative agreement with ab initio methods reducing the computational cost by about 80%.Moreover,we show that this framework naturally extends to molecular dynamics simulations,paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths.
基金support from the U.S. Department of Energy, Office of Science Basic Energy Sciences under grant DE-SC0024256. H. L. and Y. X. acknowledge the support from the US National Science Foundation through awards DMR-2317008+3 种基金. Y. X. also acknowledges the support from the Faculty Development Program at Portland State University. C.W. acknowledges support from the National Science Foundation (NSF) through award 2311203Z. L. and C. W. acknowledge computational resources from the National Energy Research Scientific Computing Center (NERSC) through award ERCAP0031557This research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. We acknowledge the computing resources provided by Bridges2 at Pittsburgh Supercomputing Center (PSC) through allocations mat220006p, mat220008p, and dmr160027p from the Advanced Cyber-infrastructure Coordination Ecosystem: ServicesSupport (ACCESS) program, which is supported by National Science Foundation grants 2138259, 2138286, 2138307, 2137603, and 2138296.
文摘Accurate first-principles prediction of lattice thermal conductivity(κ_(L))remains challenging in identifying materials with extreme thermal behavior.While the harmonic approximation with threephonon scattering(HA+3ph)is now routine,reliableκ_(L)prediction often requires higher-order anharmonic effects,including self-consistent phonon renormalization,three-and four-phonon scattering,and off-diagonal heat flux(SCPH+3,4ph+OD).We present a state-of-the-art highthroughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures.From 562 dynamically stable compounds,weassess the hierarchical impacts of higher-order anharmonicity.For around 60%of materials,HA+3ph predictions closely match those from SCPH+3,4ph+OD.SCPH generally increasesκ_(L),by over 8 times in extreme cases,whereas four-phonon scattering universally suppressesκ_(L),sometimes to 15%of the HA+3ph value.Off-diagonal contributions are negligible in high-κ_(L)systems but can rival diagonal terms in highly anharmonic low-κ_(L)compounds.We highlight four case studies,Rb_(2)TlAlH_(6),Cu_(3)VSe_(4),CuBr,and KTlCl_(4),that exhibit distinct extreme behaviors.This work delivers not only a robust workflow for high-fidelityκ_(L)dataset but also a quantitative framework to determine when higher-order effects are essential.The hierarchy ofκ_(L)results,from the HA+3ph to SCPH+3,4ph+OD level,offers a scalable,interpretable route to discovering next-generation extreme thermal materials.
文摘Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects.Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based services.To provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities.Discrete EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular way.In this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing Framework.Simulation results show that this new approach significantly,improves energy efficiency of buildings and reduces latency.Additionally,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.
基金partially supported by the National Science Centre,Poland(Nos.2022/45/B/ST6/04145 and 2021/41/B/HS6/02798)the EU under the Horizon Europe(No.101086321 OMINO).
文摘With the advancement of computational network science,its research scope has significantly expanded beyond static graphs to encompass more complex structures.The introduction of streaming,temporal,multilayer,and hypernetwork approaches has brought new possibilities and imposed additional requirements.For instance,by utilising these advancements,one can model structures such as social networks in a much more refined manner,which is particularly relevant in simulations of the spreading processes.Unfortunately,the pace of advancement is often too rapid for existing computational packages to keep up with the functionality updates.This results in a significant proliferation of tools used by researchers and,consequently,a lack of a universally accepted technological stack that would standardise experimental methods(as seen,e.g.,in machine learning).This article addresses that issue by presenting an extended version of the Network Diffusion library.First,a survey of the existing approaches and toolkits for simulating spreading phenomena is shown,and then,an overview of the framework functionalities.Finally,we report four case studies conducted with the package to demonstrate its usefulness:the impact of sanitary measures on the spread of COVID-19,the comparison of information diffusion on two temporal network models,and the effectiveness of seed selection methods in the task of influence maximisation in multilayer networks.We conclude the paper with a critical assessment of the library and the outline of still awaiting challenges to standardise research environments in computational network science.
文摘This paper investigates mixed convection heat transfer in vertical multilayer flow in a system consisting of a viscous fluid flanked by nanofluids in a porous medium,taking account of magnetohydrodynamic(MHD)and radiation effects and internal heat generation.The thermal conductivity of the nanofluids is analyzed using the Maxwell-Garnett and Patel models.A computational framework for solving the governing nonlinear differential equations using an analytical and perturbative approach is established,to provide accurate predictions of heat transfer characteristics.The interplay between the viscous fluid and the nanofluids in the presence of MHD effects introduces complex thermal and fluid dynamic interactions,highlighting the need for innovative modeling approaches.The results obtained provides enhanced understanding of multiphase flow behavior in the presence of internal heat generation and external magnetic fields.They will contribute to the development of methods for optimizing heat transfer in advanced thermal management applications such as nuclear reactor cooling,medical management of hyperthermia,and industrial energy systems.
基金funding from the European Union – NextGenerationEU instrument and the Research Council of Finland's AICon project (grant number no. 348179). The authors gratefully acknowledge CSC – IT Center for Science, Finland, and the Aalto Science-IT project for generous computational resources.
文摘Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges.Herein,we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts,using machine-learned force fields.We propose a new catalytic descriptor,termed adsorption energy distribution,that aggregates the binding energies for different catalyst facets,binding sites,and adsorbates.The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates.By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys,we offer a powerful tool for catalyst discovery.We propose new promising candidates such as ZnRh and ZnPt_(3),which to our knowledge,have not yet been tested,and discuss their possible advantage in terms of stability.
基金supported by the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 66706-DNI7Simulations and analyses were performed using resources from Princeton Research Computing at Princeton University, which is a consortium led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing. These resources include a GPU-based computing cluster purchased with support from the National Science Foundation (Grant No. NSF-MRI: OAC-2320649).
文摘Modifying solution viscosity is a key functional application of polymers,yet the interplay of molecular chemistry,polymer architecture,and intermolecular interactions makes tailoring precise rheological responses challenging.We introduce a computational framework coupling topology-aware generative machine learning,Gaussian process modeling,and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles.Targeting thirty rheological profiles of varying difficulty,Bayesian optimization identifies polymers that satisfy all lowand most medium-difficulty targets by modifying topology and solvophobicity,with other variables fixed.In these regimes,wefind and explain design degeneracy,where distinct polymers produce nearidentical rheological profiles.However,satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space;this is rationally guided by physical scaling theories.This integrated framework establishes a data-driven yet mechanistic route to rational polymer design.
基金supported by the National Natural Science Foundation ofChina(Grant 52106130)the State Key Laboratory of Explosion Science and Safety Protection(Grants QNKT23-15).
文摘The discovery and optimization of high-energy materials(HEMs)face challenges due to the computational expense and slow iteration of traditional methods.Neural network potentials(NNPs)have emerged as an efficient alternative to first-principles simulations.This study presents EMFF-2025,a general NNP model for C,H,N,and O-based HEMs,leveraging transfer learning with minimal data from DFT calculations.The model achieves DFT-level accuracy,predicting the structure,mechanical properties,and decomposition characteristics of 20 HEMs.Integrating EMFF-2025 with PCA and correlation heatmaps,we map the chemical space and structural evolution of these HEMs across temperatures.Surprisingly,EMFF-2025 uncovers that most HEMs follow similar hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.
基金the ThermOSS and HYPHAD projects. Project HYPHAD was selected in the Joint Transnational Call 2023 of M-ERA.NET 3, which is an EU-funded network of about 49 funding organisations (Horizon 2020 grant agreement No 958174). The project is funded by the Korea Institute for Advancement of Technology, South Korea, the National Science Centre, Poland, and the Sächsisches Staatsministerium für Wissenschaft, Kultur und Tourismus, Germany. This project is co-financed with tax revenue on the basis of the budget adopted by the Saxon State Parliament. This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program No. P0027799This research was partially funded by the National Science Centre, Poland, number: 2023/05/Y/ST3/00249 under the M-ERA.NET 3 Call 2023+1 种基金Project ThermOSS is funded by the European Union and is co-financed with tax revenue on the basis of the budget adopted by the Saxon State ParliamentG.G. acknowledges the support from the National Research Foundation of Korea, the Ministry of Science and ICT under award numbers (2021R1C1C2094407, RS-2024-00429941), and supercomputing resources from Korea Institute of Science and Technology Information (KISTI). The authors gratefully acknowledge the computing time made available to them on the high-performance computer at the NHR Center of TU Dresden. This center is jointly supported by the Federal Ministry of Education and Research (BMBF) and the state governments participating in the NHR.
文摘Accurate first-principles-based prediction of the pressure-composition-temperature(PCT)relationships of metal hydrides can enable predictive optimization of hydrogen capacities and pressures.In this work,we introduce a novel computational framework that integrates density functional theory(DFT)with a Python-based PCT Simulation Toolkit to predict PCT diagrams with high accuracy.By using only structural input data from the metallic phase,this toolkit automates the detection of interstitial voids,generates input files for DFT calculations,and constructs thermodynamic models based on para-equilibrium principles.We validate this approach across five major metal-hydride classes–BCC and FCC alloys,AB_(5),AB_(2),and AB compounds-and demonstrate that even with minimal computational effort,key hydrogen sorption characteristics can be reliably determined.Using the PBE functional without vibrational contribution,our results show that hydrogen capacity predictions achieve a mean accuracy of 95%,while sorption pressures are modeled within one order of magnitude of experimental values.Furthermore,our method can implicitly account for the phase transition in metal hydrides and can reliably model multicomponent alloys with representative alloys of lesser chemical complexity.This framework enables rapid and accurate exploration of metal hydrides to design alloys for new applications.
基金supported by NSF grant#1905421with computing resources provided by GM Global Technical Center and Stampede3 at TACC(ACCESS allocation DMR190035,NSF grants#2138259,#2138286,#2138307,#2137603,#2138296).The authors thank Prof.Dane Morgan from the University of Wisconsin-Madison for insightful discussions.
文摘Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales.Here,we develop an integrated computational framework combining on-lattice kinetic Monte Carlo(KMC)simulations,absorbing Markov chain models,and mesoscale cluster dynamics(CD)to investigate these interactions in Al-Mg-Zn alloys.The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy.These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching,which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging.Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics.Results provide insights to guide alloy chemistry,quench rates,and aging time at finite temperatures to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.
基金supported by the National Natural Science Foundation of China(Grant No.61471338)the Knowledge Innovation Program of the Chinese Academy of Sciences,Youth Innovation Promotion Association CAS,President Fund of UCASCRSRI Open Research Program(Grant No.CKWV2015217/KY)
文摘The three-dimensional discontinuous deformation analysis(3D-DDA) is a promising numerical method for both static and dynamic analyses of rock systems. Lacking mature software, its popularity is far behind its ability. To address this problem, this paper presents a new software architecture from a software engineering viewpoint. Based on 3D-DDA characteristics, the implementation of the proposed architecture has the following merits. Firstly, the software architecture separates data, computing, visualization, and signal control into individual modules. Secondly, data storage and parallel access are fully considered for different conditions. Thirdly, an open computing framework is provided which supports most numerical computing methods; common tools for equation solving and parallel computing are provided for further development. Fourthly, efficient visualization functions are provided by integrating a variety of visualization algorithms. A user-friendly graphical user interface is designed to improve the user experience. Finally, through a set of examples, the software is verified against both analytical solutions and the original code by Dr. Shi Gen Hua.
