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
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
Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inex...Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research focus.In this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond Perovskites.BazrS3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.展开更多
The labels of VU1 and VU2 in Fig.1(b)of the paper[Chin.Phys.B 34046801(2025)]were not correctly placed.The correct figure is provided.This modification does not affect the result presented in the paper.
Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhi...Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level,which are more easily realized in the absence of interlayer interactions.However,this absence also destabilizes the monolayer forms of many bulk kagome materials,posing significant challenges to their discovery.In this work,we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a“1+3”design framework.Through high-throughput computational screening of 349 candidate materials,we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties.These materials were classified into three categories based on their lattice geometry,symmetry,band gaps,and magnetic configurations.Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels,with orbital contributions varying between oxygen 2p and transition metal d states.This study demonstrates the feasibility of the“1+3”strategy,offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.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
Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural n...Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.展开更多
This paper deals with modeling of the phenomenon of fretting fatigue in heterogeneous materials using the multi-scale computational homogenization technique and finite element analysis(FEA).The heterogeneous material ...This paper deals with modeling of the phenomenon of fretting fatigue in heterogeneous materials using the multi-scale computational homogenization technique and finite element analysis(FEA).The heterogeneous material for the specimens consists of a single hole model(25% void/cell,16% void/cell and 10% void/cell)and a four-hole model(25%void/cell).Using a representative volume element(RVE),we try to produce the equivalent homogenized properties and work on a homogeneous specimen for the study of fretting fatigue.Next,the fretting fatigue contact problem is performed for 3 new cases of models that consist of a homogeneous and a heterogeneous part(single hole cell)in the contact area.The aim is to analyze the normal and shear stresses of these models and compare them with the results of the corresponding heterogeneous models based on the Direct Numerical Simulation(DNS)method.Finally,by comparing the computational time and%deviations,we draw conclusions about the reliability and effectiveness of the proposed method.展开更多
In all-solid-state lithium batteries,the impedance at the cathode/electrolyte interface shows close relationship with the cycle performance.Cathode coatings are helpful to reduce the impedance and increase the stabili...In all-solid-state lithium batteries,the impedance at the cathode/electrolyte interface shows close relationship with the cycle performance.Cathode coatings are helpful to reduce the impedance and increase the stability at the interface effectively.LiTi_(2)(PO_(4))_(3),a fast ion conductor with high ionic conductivity approaching 10^(-3)S·cm^(-1),is adopted as the coating materials in this study.The crystal and electronic structures,as well as the Li^+ion migration properties are evaluated for LTP and its doped derivatives based on density functional theory(DFT)and bond valence(BV)method.Substituting part of Ti sites with element Mn,Fe,or Mg in LTP can improve the electronic conductivity of LTP while does not decrease its high ionic conductivity.In this way,the coating materials with both high ionic conductivities and electronic conductivities can be prepared for all-solid-state lithium batteries to improve the ion and electron transport properties at the interface.展开更多
Lithium-ion batteries(LIBs)and lithium-sulfur(Li–S)batteries are two types of energy storage systems with significance in both scientific research and commercialization.Nevertheless,the rational design of electrode m...Lithium-ion batteries(LIBs)and lithium-sulfur(Li–S)batteries are two types of energy storage systems with significance in both scientific research and commercialization.Nevertheless,the rational design of electrode materials for overcoming the bottlenecks of LIBs and Li–S batteries(such as low diffusion rates in LIBs and low sulfur utilization in Li–S batteries)remain the greatest challenge,while two-dimensional(2D)electrodes materials provide a solution because of their unique structural and electrochemical properties.In this article,from the perspective of ab-initio simulations,we review the design of 2D electrode materials for LIBs and Li–S batteries.We first propose the theoretical design principles for 2D electrodes,including stability,electronic properties,capacity,and ion diffusion descriptors.Next,classified examples of promising 2D electrodes designed by theoretical simulations are given,covering graphene,phosphorene,MXene,transition metal sulfides,and so on.Finally,common challenges and a future perspective are provided.This review paves the way for rational design of 2D electrode materials for LIBs and Li–S battery applications and may provide a guide for future experiments.展开更多
The fine-scale heterogeneity of granular material is characterized by its polydisperse microstructure with randomness and no periodicity. To predict the mechanical response of the material as the microstructure evolve...The fine-scale heterogeneity of granular material is characterized by its polydisperse microstructure with randomness and no periodicity. To predict the mechanical response of the material as the microstructure evolves, it is demonstrated to develop computational multiscale methods using discrete particle assembly-Cosserat continuum modeling in micro- and macro- scales,respectively. The computational homogenization method and the bridge scale method along the concurrent scale linking approach are briefly introduced. Based on the weak form of the Hu-Washizu variational principle, the mixed finite element procedure of gradient Cosserat continuum in the frame of the second-order homogenization scheme is developed. The meso-mechanically informed anisotropic damage of effective Cosserat continuum is characterized and identified and the microscopic mechanisms of macroscopic damage phenomenon are revealed. c 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi： 10.1063/2.1301101]展开更多
Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- ample...Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level展开更多
Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys...Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development cost.This study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.展开更多
Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and...Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.展开更多
Stability and accuracy of the imaging results are still unmet practical demands for ultrasonic computed tomography(CT)of concrete material.To address these issues,a CT technique based on simulated annealing genetic al...Stability and accuracy of the imaging results are still unmet practical demands for ultrasonic computed tomography(CT)of concrete material.To address these issues,a CT technique based on simulated annealing genetic algorithm(SAGA)is presented in this work.Firstly,a natural weight matrix with clear physical meaning is introduced in the inverse algorithm and then a quadric broadening objective function is formed according to the propagation characteristics of ultrasound in concrete.After that,the simulated annealing(SA)searching is added to speed up the inverse process and to improve the convergence and stability of the algorithm.Finally,the optimal inverse imaging results have been achieved by variable ectopic adaptive genetic algorithm.The numerical simulation experiments have shown that the usage of the correct priori information and the excellent characteristic of SAGA in searching the global minimum value of the function have produced accurate and effective results with stable numerical values.The imaging resolution is improved and the imagining results reflecting the inner defections of the tested objects are more reliable and accurate.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
The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising altern...The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.展开更多
This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples...This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.展开更多
The rapid evolution of high-throughput theoretical design schemes to discover new lithium battery materials is re- viewed, including fiigh-capacity cathodes, low-strain cathodes, anodes, solid state eleclrolytes, and ...The rapid evolution of high-throughput theoretical design schemes to discover new lithium battery materials is re- viewed, including fiigh-capacity cathodes, low-strain cathodes, anodes, solid state eleclrolytes, and electrolyte additives. With tfie development of efficient theoretical methods and inexpensive computers, high-throughput theoretical calculations have played an increasingly important role in the discovery of new malerials. With the help of automatic simnlation flow, many types of materials can be screened, optimized and designed from a structural database according to specific search criteria. In advanced cell technology, new materials for next generation lithium batteries are of great significance to achieve perlbmmnce, and some representative criteria are： higher energy density, better safety, and faster charge/discharge speed.展开更多
Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperatur...Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.展开更多
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.
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Project supported by the National Key Research and Development Program of China(Grant No.2016YFB0700700)the National Natural Science Foundation of China(Grant Nos.11674237,11974257,and 51602211)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Chinathe Suzhou Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,China。
文摘Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research focus.In this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond Perovskites.BazrS3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.
文摘The labels of VU1 and VU2 in Fig.1(b)of the paper[Chin.Phys.B 34046801(2025)]were not correctly placed.The correct figure is provided.This modification does not affect the result presented in the paper.
基金financial support from the National Key Research&Development Program of China(Grant No.2023YFA1406500)the National Natural Science Foundation of China(Grant Nos.12104504,52461160327 and 92477205)the Fundamental Research Funds for the Central Universities,and the Research Funds of Renmin University of China[Grant Nos.22XNKJ30(W.J.)and 24XNKJ17(C.W.)]。
文摘Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level,which are more easily realized in the absence of interlayer interactions.However,this absence also destabilizes the monolayer forms of many bulk kagome materials,posing significant challenges to their discovery.In this work,we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a“1+3”design framework.Through high-throughput computational screening of 349 candidate materials,we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties.These materials were classified into three categories based on their lattice geometry,symmetry,band gaps,and magnetic configurations.Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels,with orbital contributions varying between oxygen 2p and transition metal d states.This study demonstrates the feasibility of the“1+3”strategy,offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.
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financially supported by the Ministry of Education(Singapore)(MOE-T2EP50220-0022)SUTD-MIT International Design Center(Singapore)+3 种基金SUTD-ZJU IDEA Grant Program(SUTD-ZJU(VP)201903)SUTD Kickstarter Initiative(SKI 2021_02_03,SKI 2021_02_17,SKI 2021_01_04)Agency of Science,Technology and Research(Singapore)(A20G9b0135)National Supercomputing Centre(Singapore)(15001618)。
文摘Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.
文摘This paper deals with modeling of the phenomenon of fretting fatigue in heterogeneous materials using the multi-scale computational homogenization technique and finite element analysis(FEA).The heterogeneous material for the specimens consists of a single hole model(25% void/cell,16% void/cell and 10% void/cell)and a four-hole model(25%void/cell).Using a representative volume element(RVE),we try to produce the equivalent homogenized properties and work on a homogeneous specimen for the study of fretting fatigue.Next,the fretting fatigue contact problem is performed for 3 new cases of models that consist of a homogeneous and a heterogeneous part(single hole cell)in the contact area.The aim is to analyze the normal and shear stresses of these models and compare them with the results of the corresponding heterogeneous models based on the Direct Numerical Simulation(DNS)method.Finally,by comparing the computational time and%deviations,we draw conclusions about the reliability and effectiveness of the proposed method.
基金Project supported by the National Natural Science Foundation of China(Grant No.51772321)the National Key R&D Program of China(Grant No.2017YFB0701602)the Youth Innovation Promotion Association,China(Grant No.2016005)。
文摘In all-solid-state lithium batteries,the impedance at the cathode/electrolyte interface shows close relationship with the cycle performance.Cathode coatings are helpful to reduce the impedance and increase the stability at the interface effectively.LiTi_(2)(PO_(4))_(3),a fast ion conductor with high ionic conductivity approaching 10^(-3)S·cm^(-1),is adopted as the coating materials in this study.The crystal and electronic structures,as well as the Li^+ion migration properties are evaluated for LTP and its doped derivatives based on density functional theory(DFT)and bond valence(BV)method.Substituting part of Ti sites with element Mn,Fe,or Mg in LTP can improve the electronic conductivity of LTP while does not decrease its high ionic conductivity.In this way,the coating materials with both high ionic conductivities and electronic conductivities can be prepared for all-solid-state lithium batteries to improve the ion and electron transport properties at the interface.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(PolyU152178/20 E)the Hong Kong Polytechnic University(1-W19S)Science and Technology Program of Guangdong Province of China(2020A0505090001).
文摘Lithium-ion batteries(LIBs)and lithium-sulfur(Li–S)batteries are two types of energy storage systems with significance in both scientific research and commercialization.Nevertheless,the rational design of electrode materials for overcoming the bottlenecks of LIBs and Li–S batteries(such as low diffusion rates in LIBs and low sulfur utilization in Li–S batteries)remain the greatest challenge,while two-dimensional(2D)electrodes materials provide a solution because of their unique structural and electrochemical properties.In this article,from the perspective of ab-initio simulations,we review the design of 2D electrode materials for LIBs and Li–S batteries.We first propose the theoretical design principles for 2D electrodes,including stability,electronic properties,capacity,and ion diffusion descriptors.Next,classified examples of promising 2D electrodes designed by theoretical simulations are given,covering graphene,phosphorene,MXene,transition metal sulfides,and so on.Finally,common challenges and a future perspective are provided.This review paves the way for rational design of 2D electrode materials for LIBs and Li–S battery applications and may provide a guide for future experiments.
基金supported by the National Natural Science Foundation of China(11072046,10672033,90715011 and 11102036)the National Basic Research and Development Program(973Program,2010CB731502)
文摘The fine-scale heterogeneity of granular material is characterized by its polydisperse microstructure with randomness and no periodicity. To predict the mechanical response of the material as the microstructure evolves, it is demonstrated to develop computational multiscale methods using discrete particle assembly-Cosserat continuum modeling in micro- and macro- scales,respectively. The computational homogenization method and the bridge scale method along the concurrent scale linking approach are briefly introduced. Based on the weak form of the Hu-Washizu variational principle, the mixed finite element procedure of gradient Cosserat continuum in the frame of the second-order homogenization scheme is developed. The meso-mechanically informed anisotropic damage of effective Cosserat continuum is characterized and identified and the microscopic mechanisms of macroscopic damage phenomenon are revealed. c 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi： 10.1063/2.1301101]
文摘Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level
基金supported by the National Natural Science Foundation of China(No.52073030)。
文摘Integrated computational materials engineering(ICME)is to integrate multi-scale computational simulations and key experimental methods such as macroscopic,mesoscopic,and microscopic into the whole process of Al alloys design and development,which enables the design and development of Al alloys to upgrade from traditional empirical to the integration of compositionprocess-structure-mechanical property,thus greatly accelerating its development speed and reducing its development cost.This study combines calculation of phase diagram(CALPHAD),Finite element calculations,first principle calculations,and microstructure characterization methods to predict and regulate the formation and structure of composite precipitates from the design of highmodulus Al alloy compositions and optimize the casting process parameters to inhibit the formation of micropore defects in the casting process,and the final tensile strength of Al alloys reaches420 MPa and Young's modulus reaches more than 88 GPa,which achieves the design goal of the high strength and modulus Al alloys,and establishes a new mode of the design and development of the strength/modulus Al alloys.
基金the Science Challenge Project(TZ2018004)the National Natural Science Foundation of China(21875228 and 21702195)for financial support。
文摘Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.
基金supported by the National Natural Science Foundation of China (No.11264032)the Aeronautical Science Foundation of China (No.2014ZD56007)+4 种基金the Science and Technology Project of General Administration of Quality Supervision,Inspection and Quarantine of China (No.2013zjjz180)the Natural Science Foundation of Jiangxi Province (No.20122BAB201024)the Science and Technology Project of the Education Department of Jiangxi Province (No.GJJ14530)the Innovation Foundation of Shanghai Aerospace(SAST201364)the Graduate Innovation Foundation of Nanchang Hangkong University (No.YC2013010)
文摘Stability and accuracy of the imaging results are still unmet practical demands for ultrasonic computed tomography(CT)of concrete material.To address these issues,a CT technique based on simulated annealing genetic algorithm(SAGA)is presented in this work.Firstly,a natural weight matrix with clear physical meaning is introduced in the inverse algorithm and then a quadric broadening objective function is formed according to the propagation characteristics of ultrasound in concrete.After that,the simulated annealing(SA)searching is added to speed up the inverse process and to improve the convergence and stability of the algorithm.Finally,the optimal inverse imaging results have been achieved by variable ectopic adaptive genetic algorithm.The numerical simulation experiments have shown that the usage of the correct priori information and the excellent characteristic of SAGA in searching the global minimum value of the function have produced accurate and effective results with stable numerical values.The imaging resolution is improved and the imagining results reflecting the inner defections of the tested objects are more reliable and accurate.
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.
基金This work was supported by the National Research Foundation,Singapore under Award No.NRF-CRP24-2020-0002.
文摘The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.
基金Project supported by the National Natural Science Foundation of China(Grant No.51772321)the Beijing Science and Technology Project(Grant No.D171100005517001)+1 种基金the National Key Research and Development Plan(Grant No.2017YFB0701602)the Youth Innovation Promotion Association(Grant No.2016005)
文摘This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.11234013 and 51172274)the National High Technology Research and Development Program of China(Grant No.2015AA034201)
文摘The rapid evolution of high-throughput theoretical design schemes to discover new lithium battery materials is re- viewed, including fiigh-capacity cathodes, low-strain cathodes, anodes, solid state eleclrolytes, and electrolyte additives. With tfie development of efficient theoretical methods and inexpensive computers, high-throughput theoretical calculations have played an increasingly important role in the discovery of new malerials. With the help of automatic simnlation flow, many types of materials can be screened, optimized and designed from a structural database according to specific search criteria. In advanced cell technology, new materials for next generation lithium batteries are of great significance to achieve perlbmmnce, and some representative criteria are： higher energy density, better safety, and faster charge/discharge speed.
基金the Johns Hopkins University Leong Research Award for Undergraduates.
文摘Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.
