This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carb...The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.展开更多
Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is...Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.展开更多
MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, Mat...MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities： a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.展开更多
Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancemen...Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.展开更多
Oxide double perovskites A2 B’B"O6 are a class of emerging materials in the fields of optoelectronics and catalysis.Due to the chemical flexibilities of perovskite structures,there are multiple elemental combina...Oxide double perovskites A2 B’B"O6 are a class of emerging materials in the fields of optoelectronics and catalysis.Due to the chemical flexibilities of perovskite structures,there are multiple elemental combinations of cations A,B’,and B",which leading to tremendous candidates.In this study,we comprehensively screened stable oxide double perovskite A2 B’B"O6 from a pool of 2,018 perovskite candidates using a high-throughput computational approach.By considering a tolerance factor(t)-octahedral factor(μ) phase diagram,138 candidates with Fm 3 m, P21/c,and R3 c phases were selected and systematically studied via first-principles calculations based on density functional theory.The screening procedure finally predicted the existence of 21 stable perovskites,and 14 among them have never been reported.Verification with existing experimental results demonstrates that the prediction accuracy for perovskite formability is approximately 90%.The predicted oxide double perovskites exhibit quasi-direct bandgaps ranging from 0 to 4.4 eV with a significantly small direct-indirect bandgap difference,balanced electron and hole effective masses,and strong optical absorptions.The newly predicted oxide double perovskites may enlarge the pool of material candidates for applications in optoelectronics and photocatalysis.This study provides a route for computational screening of novel perovskites for functional applications.展开更多
Cost effective separation of acetylene(C_2H_2)and ethylene(C_2H_4)is of key importance to obtain essential chemical raw materials for polymer industry.Due to the low compression limit of C_2H_2,there is an urgent dema...Cost effective separation of acetylene(C_2H_2)and ethylene(C_2H_4)is of key importance to obtain essential chemical raw materials for polymer industry.Due to the low compression limit of C_2H_2,there is an urgent demand to develop suitable materials for efficiently separating the two gases under ambient conditions.In this paper,we provided a high-throughput screening strategy to study porous metal-organic frameworks(MOFs)containing open metal sites(OMS)for C_2H_2/C_2H_4 separation,followed by a rational design of novel MOFs in-silico.A set of accurate force fields was established from ab initio calculations to describe the critical role of OMS towards vip molecules.From a large-scale computational screening of 916 experimental Cu-paddlewheel-based MOFs,three materials were identified with excellent separation performance.The structure-performance relationships revealed that the optimal materials should have the largest cavity diameter around 5-10?and pore volume in-between 0.3-1.0 cm^3 g^(-1).Based on the systematic screening study result,three novel MOFs were further designed with the incorporation of fluorine functional group.The results showed that Cu-OMS and the-F group on the aromatic rings close to Cu sites could generate a synergistic effect on the preferential adsorption of C_2H_2 over C_2H_4,leading to a remarkable improvement of C_2H_2 separation performance of the materials.The findings could provide insight for future experimental design and synthesis of high-performance nanostructured materials for C_2H_2/C_2H_4 separation.展开更多
Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due ...Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.展开更多
For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered tr...For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.展开更多
Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale produ...Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale production.In this work,the competitive adsorption selectivity of CO_(2)in a five-component gas mixture of tens of thousands of porous materials was calculated based on high-throughput screening and grand canonical Monte Carlo simulation.Seven promising CO_(2)-type adsorbents were obtained under equimolar and industrial components,among which RUBTAK03 had a higher adsorption selectivity between 65 and 75.The CO_(2)adsorption capacity of KINNIG under a single component was 8.72 mmol/g at 298 K and 1 bar,surpassing most well-known metal–organic frameworks.This strong CO_(2)capture performance originates from three-dimensional interlaced channels,fluorinated organic ligands,and ultra-micropores,including channels and cages.In particular,this type of porous material composed of organic ligands or inorganic pillars containing fluorine atoms achieves an efficient capture of CO_(2)from air and industrial tail gas,providing theoretical guidance for the design of novel and efficient adsorbents.展开更多
Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in th...Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by pure phase,uniform size distribution,and highly preferred orientation,QD-based memristors demonstrate a 57%reduction in switching voltage,a two-order-of-magnitude increase in the ON/OFF ratio,and endurance and retention degradation as low as 0.1%over 8.4×10^(7)s of continuous operation and 10^(5)rapid read cycles.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved by 477%and 27.8%(reaching 92.23%),respectively.These findings establish a scalable,data-driven strategy for rational design of QD-based memristors,advancing the development of next-generation reliable neuromorphic computing systems.展开更多
Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narr...Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this neces...In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.展开更多
Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase(PI3K),and RAS/BRAF pathways.In this work,through diversity-based high-throughput virtual screening ...Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase(PI3K),and RAS/BRAF pathways.In this work,through diversity-based high-throughput virtual screening we identified a lead molecule that selectively targets PI3K and BRAF^(V600E) kinases.Computational screening,Molecular dynamics simulation and MMPBSA calculations were performed.PI3K and BRAF^(V600E) kinase inhibition was done.A375 and G-361 cells were used for in vitro cellular analysis to determine antiproliferative effects,annexin V binding,nuclear fragmentation and cell cycle analysis.Computational screening of small molecules indicates compound CB-006-3 selectively targets PI3KCG(gamma subunit),PI3KCD(delta subunit)and BRAF^(V600E).Molecular dynamics simulation and MMPBSA bases binding free energy calculations predict a stable binding of CB-006-3 to the active sites of PI3K and BRAF^(V600E).The compound effectively inhibited PI3KCG,PI3KCD and BRAF^(V600E)kinases with respective IC50 values of 75.80,160.10 and 70.84 nM.CB-006-3 controlled the proliferation of A375 and G-361 cells with GI50 values of 223.3 and 143.6 nM,respectively.A dose dependent increase in apoptotic cell population and sub G0/G1 phase of cell cycle were also observed with the compound treatment in addition to observed nuclear fragmentation in these cells.Furthermore,CB-006-3 inhibited BRAF^(V600E),PI3KCD and PI3KCG in both melanoma cells.Collectively,based on the computational modeling and in vitro validations,we propose CB-006-3 as a lead candidate for selectively targeting PI3K and mutant BRAF^(V600E) to inhibit melanoma cell proliferation.Further experimental validations,including pharmacokinetic evaluations in mouse models will identify the druggability of the proposed lead candidate for further development as a therapeutic agent for treating melanoma.展开更多
The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digit...The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.展开更多
Additives are widely employed to regulate the morphology,size,and agglomeration degree of crystalline materials during crystallization to enhance their functional,physical,and powder properties.However,the existing me...Additives are widely employed to regulate the morphology,size,and agglomeration degree of crystalline materials during crystallization to enhance their functional,physical,and powder properties.However,the existing methods for screening and validating target additives require a large quantity of materials and involve tedious molecular simulation/crystallization experiments,making them time-consuming,resource-intensive,and reliant on the operator’s experience level.To overcome these challenges,we proposed a computer vision-assisted high-throughput additive screening system(CV-HTPASS)which comprises a high-throughput additive screening device,in situ imaging equipment,and an artificial intelligence(AI)-assisted image-analysis algorithm.Using the CV-HTPASS,we performed high-throughput screening experiments on additives to regulate the succinic acid crystal properties,generating thousands of crystal images with diverse crystal morphologies.To extract valuable crystal information from the massive data and improve the analysis accuracy and efficiency,the AI-based image-analysis algorithm was implemented innovatively for the segmentation,classification,and data mining of crystals with four morphologies to further screen the target additive.Subsequently,scale-up crystallization experiments conducted under optimized conditions demonstrated that succinic acid products exhibited a preferred cubic morphology,reduced agglomeration degree,narrowed crystal size distribution,and improved powder properties.The proposed CV-HTPASS offers a highly efficient approach for scale-up experiments.Further,it provides a platform for the screening of additives and the optimization of the powder properties of crystal products in industrial-scale crystallization processes.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,...This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.展开更多
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
基金supported by the Natural Science Foundation of China (Nos.21706106,21536001 and 21322603)the National Key Basic Research Program of China ("973") (No.2013CB733503)+1 种基金the Natural Science Foundation of Jiangsu Normal University(16XLR011)Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.
基金The authors are grateful for financial support from the National Key Projects for Fundamental Research and Development of China(2021YFA1500803)the National Natural Science Foundation of China(51825205,52120105002,22102202,22088102,U22A20391)+1 种基金the DNL Cooperation Fund,CAS(DNL202016)the CAS Project for Young Scientists in Basic Research(YSBR-004).
文摘Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2017YFB0701702 and 2016YFB0700501)the National Natural Science Foundation of China(Grant Nos.61472394 and 11534012)Science and Technology Department of Sichuan Province,China(Grant No.2017JZ0001)
文摘MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities： a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.
文摘Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.
基金the funding support from the National Key Research and Development Program of China(Grant 2016YFB0700700)National Natural Science Foundation of China(Grants 11674237,11974257)+1 种基金Priority Academic program Development of Jiangsu Higher Education Institutions(PAPD)Suzhou Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,China。
文摘Oxide double perovskites A2 B’B"O6 are a class of emerging materials in the fields of optoelectronics and catalysis.Due to the chemical flexibilities of perovskite structures,there are multiple elemental combinations of cations A,B’,and B",which leading to tremendous candidates.In this study,we comprehensively screened stable oxide double perovskite A2 B’B"O6 from a pool of 2,018 perovskite candidates using a high-throughput computational approach.By considering a tolerance factor(t)-octahedral factor(μ) phase diagram,138 candidates with Fm 3 m, P21/c,and R3 c phases were selected and systematically studied via first-principles calculations based on density functional theory.The screening procedure finally predicted the existence of 21 stable perovskites,and 14 among them have never been reported.Verification with existing experimental results demonstrates that the prediction accuracy for perovskite formability is approximately 90%.The predicted oxide double perovskites exhibit quasi-direct bandgaps ranging from 0 to 4.4 eV with a significantly small direct-indirect bandgap difference,balanced electron and hole effective masses,and strong optical absorptions.The newly predicted oxide double perovskites may enlarge the pool of material candidates for applications in optoelectronics and photocatalysis.This study provides a route for computational screening of novel perovskites for functional applications.
基金Financial support by the Fundamental Research Funds for the Central Universities(No.buctrc201727)the Natural Science Foundation of China(No.21536001,21722602,and 21322603)。
文摘Cost effective separation of acetylene(C_2H_2)and ethylene(C_2H_4)is of key importance to obtain essential chemical raw materials for polymer industry.Due to the low compression limit of C_2H_2,there is an urgent demand to develop suitable materials for efficiently separating the two gases under ambient conditions.In this paper,we provided a high-throughput screening strategy to study porous metal-organic frameworks(MOFs)containing open metal sites(OMS)for C_2H_2/C_2H_4 separation,followed by a rational design of novel MOFs in-silico.A set of accurate force fields was established from ab initio calculations to describe the critical role of OMS towards vip molecules.From a large-scale computational screening of 916 experimental Cu-paddlewheel-based MOFs,three materials were identified with excellent separation performance.The structure-performance relationships revealed that the optimal materials should have the largest cavity diameter around 5-10?and pore volume in-between 0.3-1.0 cm^3 g^(-1).Based on the systematic screening study result,three novel MOFs were further designed with the incorporation of fluorine functional group.The results showed that Cu-OMS and the-F group on the aromatic rings close to Cu sites could generate a synergistic effect on the preferential adsorption of C_2H_2 over C_2H_4,leading to a remarkable improvement of C_2H_2 separation performance of the materials.The findings could provide insight for future experimental design and synthesis of high-performance nanostructured materials for C_2H_2/C_2H_4 separation.
文摘Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2022R1F1A1074339)。
文摘For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.
基金supported by the National Natural Science Foundation of China(12174450 and 11874429)the National Talents Program of China,Science and Technology Innovation Program of Hunan Province(2024RC1013)+6 种基金the Key Project of Natural Science Foundation of Hunan Province(Grant No.2024JJ3029)the Hunan Provincial Key Research and Development Program(Grant No.2022WK2002)the Distinguished Youth Foundation(2020JJ2039)the Project of High-Level Talents Accumulation(2018RS3021)the Program of Hundreds of Talents of Hunan Province,State Key Laboratory of Powder Metallurgy,Start-up Funding and Innovation-Driven Plan(2019CX023)of Central South University,Postgraduate Scientific Research Innovation Project of Hunan Province(CX20230104 and CX20220252)the Youth Student Program of Hunan Provincial Natural Science Foundation(Grant No.2025JJ60853)Calculations were performed at High-Performance Computing facilities of Central South University.
文摘Fischer-Tropsch synthesis is an important method for producing clean fuels and fine chemicals,but by-products such as CO_(2)bring severe challenges of low energy utilization and air pollution in commercial-scale production.In this work,the competitive adsorption selectivity of CO_(2)in a five-component gas mixture of tens of thousands of porous materials was calculated based on high-throughput screening and grand canonical Monte Carlo simulation.Seven promising CO_(2)-type adsorbents were obtained under equimolar and industrial components,among which RUBTAK03 had a higher adsorption selectivity between 65 and 75.The CO_(2)adsorption capacity of KINNIG under a single component was 8.72 mmol/g at 298 K and 1 bar,surpassing most well-known metal–organic frameworks.This strong CO_(2)capture performance originates from three-dimensional interlaced channels,fluorinated organic ligands,and ultra-micropores,including channels and cages.In particular,this type of porous material composed of organic ligands or inorganic pillars containing fluorine atoms achieves an efficient capture of CO_(2)from air and industrial tail gas,providing theoretical guidance for the design of novel and efficient adsorbents.
基金supported by the National Natural Science Foundation of China(51572205,52372159)the Natural Science Foundation Innovation Research Team of Hainan Province(524CXTD431)+1 种基金the National Science Fund for Distinguished Young Scholars of Hubei Province(201CFA067)the National Innovation and Entrepreneurship Training Program for College Students(S202510497020,202510497003,and S202510497010)。
文摘Quantum dot(QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance.However,the stochastic nature of QD structures results in the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by pure phase,uniform size distribution,and highly preferred orientation,QD-based memristors demonstrate a 57%reduction in switching voltage,a two-order-of-magnitude increase in the ON/OFF ratio,and endurance and retention degradation as low as 0.1%over 8.4×10^(7)s of continuous operation and 10^(5)rapid read cycles.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved by 477%and 27.8%(reaching 92.23%),respectively.These findings establish a scalable,data-driven strategy for rational design of QD-based memristors,advancing the development of next-generation reliable neuromorphic computing systems.
基金financial support from the Natural Science Foundation of China(No.21973064 to DX and No.22173064 to MY).
文摘Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900504).
文摘In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant No.R.G.P.1/191/43.
文摘Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase(PI3K),and RAS/BRAF pathways.In this work,through diversity-based high-throughput virtual screening we identified a lead molecule that selectively targets PI3K and BRAF^(V600E) kinases.Computational screening,Molecular dynamics simulation and MMPBSA calculations were performed.PI3K and BRAF^(V600E) kinase inhibition was done.A375 and G-361 cells were used for in vitro cellular analysis to determine antiproliferative effects,annexin V binding,nuclear fragmentation and cell cycle analysis.Computational screening of small molecules indicates compound CB-006-3 selectively targets PI3KCG(gamma subunit),PI3KCD(delta subunit)and BRAF^(V600E).Molecular dynamics simulation and MMPBSA bases binding free energy calculations predict a stable binding of CB-006-3 to the active sites of PI3K and BRAF^(V600E).The compound effectively inhibited PI3KCG,PI3KCD and BRAF^(V600E)kinases with respective IC50 values of 75.80,160.10 and 70.84 nM.CB-006-3 controlled the proliferation of A375 and G-361 cells with GI50 values of 223.3 and 143.6 nM,respectively.A dose dependent increase in apoptotic cell population and sub G0/G1 phase of cell cycle were also observed with the compound treatment in addition to observed nuclear fragmentation in these cells.Furthermore,CB-006-3 inhibited BRAF^(V600E),PI3KCD and PI3KCG in both melanoma cells.Collectively,based on the computational modeling and in vitro validations,we propose CB-006-3 as a lead candidate for selectively targeting PI3K and mutant BRAF^(V600E) to inhibit melanoma cell proliferation.Further experimental validations,including pharmacokinetic evaluations in mouse models will identify the druggability of the proposed lead candidate for further development as a therapeutic agent for treating melanoma.
文摘The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.
基金supported by the Shandong Provincial Key Research and Development Program(Major Key Technology Project)(2021CXGC010514)the National Natural Science Foundation of China(22008173).
文摘Additives are widely employed to regulate the morphology,size,and agglomeration degree of crystalline materials during crystallization to enhance their functional,physical,and powder properties.However,the existing methods for screening and validating target additives require a large quantity of materials and involve tedious molecular simulation/crystallization experiments,making them time-consuming,resource-intensive,and reliant on the operator’s experience level.To overcome these challenges,we proposed a computer vision-assisted high-throughput additive screening system(CV-HTPASS)which comprises a high-throughput additive screening device,in situ imaging equipment,and an artificial intelligence(AI)-assisted image-analysis algorithm.Using the CV-HTPASS,we performed high-throughput screening experiments on additives to regulate the succinic acid crystal properties,generating thousands of crystal images with diverse crystal morphologies.To extract valuable crystal information from the massive data and improve the analysis accuracy and efficiency,the AI-based image-analysis algorithm was implemented innovatively for the segmentation,classification,and data mining of crystals with four morphologies to further screen the target additive.Subsequently,scale-up crystallization experiments conducted under optimized conditions demonstrated that succinic acid products exhibited a preferred cubic morphology,reduced agglomeration degree,narrowed crystal size distribution,and improved powder properties.The proposed CV-HTPASS offers a highly efficient approach for scale-up experiments.Further,it provides a platform for the screening of additives and the optimization of the powder properties of crystal products in industrial-scale crystallization processes.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
基金partly funded by a BIST Ignite Programme grant from the Barcelona Institute of Science and Technology(Code:MOLOPEC)financial support from LICROX and SOREC2 EUFunded projects(Codes:951843 and 101084326)+7 种基金the BIST Program,and Severo Ochoa Programpartially funded by CEX2019-000910-S(MCIN/AEI/10.13039/501100011033 and PID2020-112650RBI00),Fundació Cellex,Fundació Mir-PuigGeneralitat de Catalunya through CERCAfunding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441financial support by the Agencia Estatal de Investigación(grant PRE2018-084881)the financial support by from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101081441support from the MCIN/AEI JdC-F Fellowship(FJC2020-043223-I)the Severo Ochoa Excellence Postdoctoral Fellowship(CEX2019-000910-S).
文摘This study first demonstrates the potential of organic photoabsorbing blends in overcoming a critical limitation of metal oxide photoanodes in tandem modules:insufficient photogenerated current.Various organic blends,including PTB7-Th:FOIC,PTB7-Th:O6T-4F,PM6:Y6,and PM6:FM,were systematically tested.When coupled with electron transport layer(ETL)contacts,these blends exhibit exceptional charge separation and extraction,with PM6:Y6 achieving saturation photocurrents up to 16.8 mA cm^(-2) at 1.23 VRHE(oxygen evolution thermodynamic potential).For the first time,a tandem structure utilizing organic photoanodes has been computationally designed and fabricated and the implementation of a double PM6:Y6 photoanode/photovoltaic structure resulted in photogenerated currents exceeding 7mA cm^(-2) at 0 VRHE(hydrogen evolution thermodynamic potential)and anodic current onset potentials as low as-0.5 VRHE.The herein-presented organic-based approach paves the way for further exploration of different blend combinations to target specific oxidative reactions by selecting precise donor/acceptor candidates among the multiple existing ones.
