Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this proc...Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.展开更多
Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a ...Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a chemical-environment-directing dataset, a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/A, respectively, and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties(radial distribution function, density, and elastic modulus), with results being well consistent with experimental values. In particular, the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol(<8%), which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems, providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.展开更多
Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature govern...Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.展开更多
Wound field switched flux(WFSF)machines exhibits characteristics of the simple robust rotor,flexible flux-adjustable capability,and no risk of demagnetization.However,they suffer from a poor torque density compared wi...Wound field switched flux(WFSF)machines exhibits characteristics of the simple robust rotor,flexible flux-adjustable capability,and no risk of demagnetization.However,they suffer from a poor torque density compared with permanent magnet machines due to the saturation.Therefore,in this paper,two WFSF machines with single-and double-layer DC windings,respectively,are optimized for the maximum torque.The end-winding(EW)lengths differ in these two machines,which can affect the optimal design.Design parameters including the DC to armature winding copper loss ratio,slot area ratio and split ratio are optimized when two machines have the same copper loss and overall sizes.In addition,the influence of the flux density ratio,total copper loss,air-gap length and aspect ratio on the optimal split ratio is investigated using the finite element method and results are explained through the analytical model accounting for the saturation.It is discovered that the EWs have no effect on the optimal copper loss ratio,which is unity.In terms of the slot area ratio,the machine with single-layer DC windings prefers smaller DC slot areas than armature slot areas.In the WFSF machine with longer EWs,the optimal split ratio becomes smaller.Moreover,compared with other parameters,the flux density ratio can significantly affect the optimal split ratio.展开更多
This study proposes a novel asymmetric rotor pole design for wound field synchronous machines(WFSMs),which can achieve high saliency ratio and also low torque ripple.The key point is the optimal design of the asymmetr...This study proposes a novel asymmetric rotor pole design for wound field synchronous machines(WFSMs),which can achieve high saliency ratio and also low torque ripple.The key point is the optimal design of the asymmetric rotor pole with the inverse-cosine-shaped(ICS)plus reverse 3rd harmonic shaping.The asymmetric rotor pole can help to improve the average output torque by enhancing the saliency ratio.The reverse 3rd harmonic shaping on the rotor pole surface is mainly used to reduce the torque ripple.To certify the effectivity of the proposed design,three-phase 54-slot/6-pole 4.7kW WFSMs with uniform air gap and with non-uniform air gap shaped by the ICS plus optimum reverse 3rd harmonic are utilized as the basic model and referenced model for comparison.For the referenced model,the optimum amplitude of reverse 3rd harmonic is preferred as 1/6.Finally,all electromagnetic characteristics of the investigated machines are predicted by the finite-element method(FEM).The highest saliency ratio and comparatively low torque ripple have been verified.展开更多
In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensivel...In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.展开更多
The transverse rupture strength(TRS)is a key mechanical property for brittle Ti(C,N)-based cermets.However,the complicated structure with the ceramics hard phase and the metal binder phase makes it challenging to asce...The transverse rupture strength(TRS)is a key mechanical property for brittle Ti(C,N)-based cermets.However,the complicated structure with the ceramics hard phase and the metal binder phase makes it challenging to ascertain the essential impact on the TRS.To fundamentally understand the fracture in this system,herein,we present a theoretical model to investigate the essential TRS including friction stress at the phase boundary and Peierls stress of dislocations based on the first-principles method,in which the strain-stress method and Peierls-Nabarro method are employed.The traditional application of this method is analyzing the contributions to the yield strength(YS)in the constitution equation.In this article,we present a transformation of the TRS calculation into YS calculation in terms of some basic mechanical theories,thereby extending the applicability of this method to the calculation of TRS.And,the numerical valuations with good linear fit between the experimental TRS and calculated TRS including intrinsic strength,Hall-Petch effect,as well as dislocation density hardening provide solid evidence for the accuracy of our deductions and theoretical model.Finally,it is evident that the brittle components in cermets are not typically the ceramic phase itself.Our methodology illuminates a novel yet effective approach for analyzing fracture in Ti(C,N)-based cermets,which may be extended to other brittle materials in future.展开更多
Ionic liquids(ILs)have exhibited great application potential in many fields due to their unique properties.Molecular dynamics(MD)simulation has been widely employed to investigate their microscopic structure.However,c...Ionic liquids(ILs)have exhibited great application potential in many fields due to their unique properties.Molecular dynamics(MD)simulation has been widely employed to investigate their microscopic structure.However,classical molecular dynamics simulations struggle to accurately describe the complex interactions in ILs using the existing parameterized force fields.Recently,the MD simulations based on machine learning force fields(MLFFs)trained by first-principles calculations have attracted considerable attentions due to their abilities to balance computational accuracy and efficiency.Herein,we report the Bayesian-based MLFFs which can be successfully applied in IL systems and accelerate MD simulation.The calculated atomic forces,structures,and vibrational behaviors were validated to match the accuracy of firstprinciples calculations.Properties of the imidazolium-based ILs,including density,self-diffusion coefficients,viscosity,and radial distribution functions were predicted at the extended scales.Z-bonds that describe the unique structures in ILs were analyzed and the influences of Cpositions,temperature,and solvent H2O on Z-bonding configurations were systematically investigated.Our results confirmed that MLFFs presented the strong feasibility to investigate the large and complex systems,especially to predict structures and properties of the ILs.And the procedure described for MLFFs provides valuable guidance for researchers who are studying ILs.展开更多
Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota compo...Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.展开更多
SiC particle reinforced Al matrix composites(SiC_(p)/Al MMCs)have been widely used in aerospace and other fields due to their excellent mechanical properties,and their machined surface integrity is crucial for the use...SiC particle reinforced Al matrix composites(SiC_(p)/Al MMCs)have been widely used in aerospace and other fields due to their excellent mechanical properties,and their machined surface integrity is crucial for the use of new generation high-tech equipment.In order to enhance the understanding and regulation of machined surface integrity in Al matrix composites,this article provides a comprehensive review of the research advancements regarding influential factors,damage characteristics,creation techniques for machined surfaces,as well as technologies for controlling machined surface integrity both domestically and internationally.The present study discusses the key issues and solutions in the processing of aluminum matrix composite materials,along with examining the extent and mechanism of various energy field assistance influence on the surface integrity of mechanically processed aluminum matrix composites.Ultimately,this article proposes future research prospects for achieving high surface integrity machining of aluminum matrix composites.展开更多
This review presents battery design automation(BDA)as a transformative artificial intelligence(AI)-driven paradigm for the next-generation lithium-ion battery research and development.Addressing the intricacy of the p...This review presents battery design automation(BDA)as a transformative artificial intelligence(AI)-driven paradigm for the next-generation lithium-ion battery research and development.Addressing the intricacy of the problems and challenges in developing lithium-ion batteries with better performance,which are cross-scale,long-process,and multi-factor,BDA integrates multi-scale simulations and artificial intelligence into a unified platform.It ranges from atomic-scale material screening to system-level performance prediction.By bridging the gap between scientific innovation and industrial applications,BDA facilitates the development of lithium-ion battery,enhancing its efficiency,safety,and energy density.The paper outlines BDA's architecture,core technologies,current progress,and future challenges,highlighting its potential to revolutionize the battery design process and strengthen the pivotal role of lithium-ion battery in energy storage technology.展开更多
基金funding support from the National Natural Science Foundation of China(92472109,T2325012)the Program for HUST Academic Frontier Youth Team+1 种基金support from the Fundamental Research Funds for the Central Universities(HUST,5003120083)supported by the Postdoctoral Fellowship Program of CPSF(GZC20240532)。
文摘Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.
基金supported by the National Natural Science Foundation of China(Nos.52303116,52403125)the Natural Science Foundation of Hunan Province(No.2024JJ6461)+2 种基金the Science and Technology Innovation Program of Hunan Province(Nos.2022RC1080,2023RC3006)the Innovation Research Foundation of NUDT(Nos.22-ZZCX-076 and 23-ZZCX-ZZGC-01-10)the Key Research and Development Program of Hunan Province of China(No.2023ZJ1040).
文摘Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a chemical-environment-directing dataset, a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/A, respectively, and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties(radial distribution function, density, and elastic modulus), with results being well consistent with experimental values. In particular, the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol(<8%), which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems, providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.
基金supported by the National Natural Science Foundation of China(No.22273002)the National Key Research and Development Program of China(No.2022YFB4101401).We acknowledge the High-performance Computing Platform of Peking University for providing the computational facility.
文摘Bimetallic surfaces play a pivotal role in heterogeneous catalysis,yet their theoretical modeling has long been hindered by the computational chal-lenges of capturing configurational disorder,a critical feature governing their catalytic properties.Tradition-al approaches rely on oversimplified ordered surface models or restrict dis-order to a few atomic layers,limiting their predictive power.Here,we Cu_(1-x)Zn_(x)Cu_(1-x)Zn_(x)present an accurate and efficient computational framework that integrates machine learning force fields(MLFFs)with the cluster expansion(CE)method to study configurationally dis-ordered bimetallic surfaces at finite temperatures.We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol,and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction.By treating bulk and surface clusters separately,we can build CE models for surface slabs with an arbitrary number of layers.Using as a case study,our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the under-standing of catalytic properties of surfaces.This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts.
基金supported in part by the National Key R&D Program of China under 2019YFB1503700by the National Natural Science Foundation of China under Grant 51677169。
文摘Wound field switched flux(WFSF)machines exhibits characteristics of the simple robust rotor,flexible flux-adjustable capability,and no risk of demagnetization.However,they suffer from a poor torque density compared with permanent magnet machines due to the saturation.Therefore,in this paper,two WFSF machines with single-and double-layer DC windings,respectively,are optimized for the maximum torque.The end-winding(EW)lengths differ in these two machines,which can affect the optimal design.Design parameters including the DC to armature winding copper loss ratio,slot area ratio and split ratio are optimized when two machines have the same copper loss and overall sizes.In addition,the influence of the flux density ratio,total copper loss,air-gap length and aspect ratio on the optimal split ratio is investigated using the finite element method and results are explained through the analytical model accounting for the saturation.It is discovered that the EWs have no effect on the optimal copper loss ratio,which is unity.In terms of the slot area ratio,the machine with single-layer DC windings prefers smaller DC slot areas than armature slot areas.In the WFSF machine with longer EWs,the optimal split ratio becomes smaller.Moreover,compared with other parameters,the flux density ratio can significantly affect the optimal split ratio.
文摘This study proposes a novel asymmetric rotor pole design for wound field synchronous machines(WFSMs),which can achieve high saliency ratio and also low torque ripple.The key point is the optimal design of the asymmetric rotor pole with the inverse-cosine-shaped(ICS)plus reverse 3rd harmonic shaping.The asymmetric rotor pole can help to improve the average output torque by enhancing the saliency ratio.The reverse 3rd harmonic shaping on the rotor pole surface is mainly used to reduce the torque ripple.To certify the effectivity of the proposed design,three-phase 54-slot/6-pole 4.7kW WFSMs with uniform air gap and with non-uniform air gap shaped by the ICS plus optimum reverse 3rd harmonic are utilized as the basic model and referenced model for comparison.For the referenced model,the optimum amplitude of reverse 3rd harmonic is preferred as 1/6.Finally,all electromagnetic characteristics of the investigated machines are predicted by the finite-element method(FEM).The highest saliency ratio and comparatively low torque ripple have been verified.
基金This work is partially supported by Guangdong Welling Motor Manufacturing Co.,Ltd and Guangdong Innovative Research Team Program(No.2011N084)China,Valeo Electrical Systems,France,and the Royal Academy of Engineering/Siemens Research Chair Program,UK.
文摘In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.
基金financially supported by the National Key R&D Program of China(no.2022YFB3706600)the Central Fund to Guide Local Scientific and Technological Development,China(no.20231ZDH04044).
文摘The transverse rupture strength(TRS)is a key mechanical property for brittle Ti(C,N)-based cermets.However,the complicated structure with the ceramics hard phase and the metal binder phase makes it challenging to ascertain the essential impact on the TRS.To fundamentally understand the fracture in this system,herein,we present a theoretical model to investigate the essential TRS including friction stress at the phase boundary and Peierls stress of dislocations based on the first-principles method,in which the strain-stress method and Peierls-Nabarro method are employed.The traditional application of this method is analyzing the contributions to the yield strength(YS)in the constitution equation.In this article,we present a transformation of the TRS calculation into YS calculation in terms of some basic mechanical theories,thereby extending the applicability of this method to the calculation of TRS.And,the numerical valuations with good linear fit between the experimental TRS and calculated TRS including intrinsic strength,Hall-Petch effect,as well as dislocation density hardening provide solid evidence for the accuracy of our deductions and theoretical model.Finally,it is evident that the brittle components in cermets are not typically the ceramic phase itself.Our methodology illuminates a novel yet effective approach for analyzing fracture in Ti(C,N)-based cermets,which may be extended to other brittle materials in future.
基金supported by the National Natural Science Foundation of China(Nos.22278397)the Fundamental Research Funds for the Central Universities(2024SMECP01).
文摘Ionic liquids(ILs)have exhibited great application potential in many fields due to their unique properties.Molecular dynamics(MD)simulation has been widely employed to investigate their microscopic structure.However,classical molecular dynamics simulations struggle to accurately describe the complex interactions in ILs using the existing parameterized force fields.Recently,the MD simulations based on machine learning force fields(MLFFs)trained by first-principles calculations have attracted considerable attentions due to their abilities to balance computational accuracy and efficiency.Herein,we report the Bayesian-based MLFFs which can be successfully applied in IL systems and accelerate MD simulation.The calculated atomic forces,structures,and vibrational behaviors were validated to match the accuracy of firstprinciples calculations.Properties of the imidazolium-based ILs,including density,self-diffusion coefficients,viscosity,and radial distribution functions were predicted at the extended scales.Z-bonds that describe the unique structures in ILs were analyzed and the influences of Cpositions,temperature,and solvent H2O on Z-bonding configurations were systematically investigated.Our results confirmed that MLFFs presented the strong feasibility to investigate the large and complex systems,especially to predict structures and properties of the ILs.And the procedure described for MLFFs provides valuable guidance for researchers who are studying ILs.
基金supported by the“Strategic Priority Research Program”of the Chinese Academy of Sciences(XDB11020700)CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows(2016LH00012)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(QYZDB-SSW-SMC021)the National Natural Science Foundation of China(31772400)
文摘Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.
基金financially supported by the National Natural Science Foundation of China(Nos.92160301,92060203,52175415 and 52205475)the Science Center for Gas Turbine Project(Nos.P2022-AB-IV-002-001 and P2023-B-IV-003-001)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK20210295)the Superior Postdoctoral Project of Jiangsu Province(No.2022ZB215)the National Key Laboratory of Science and Technology on Helicopter Transmission(Nanjing University of Aeronautics and Astronautics)(No.HTL-A-22G12).
文摘SiC particle reinforced Al matrix composites(SiC_(p)/Al MMCs)have been widely used in aerospace and other fields due to their excellent mechanical properties,and their machined surface integrity is crucial for the use of new generation high-tech equipment.In order to enhance the understanding and regulation of machined surface integrity in Al matrix composites,this article provides a comprehensive review of the research advancements regarding influential factors,damage characteristics,creation techniques for machined surfaces,as well as technologies for controlling machined surface integrity both domestically and internationally.The present study discusses the key issues and solutions in the processing of aluminum matrix composite materials,along with examining the extent and mechanism of various energy field assistance influence on the surface integrity of mechanically processed aluminum matrix composites.Ultimately,this article proposes future research prospects for achieving high surface integrity machining of aluminum matrix composites.
基金supported by the Advanced Materials-National Science and Technology Major Project(2025ZD0618801)the National Natural Science Foundation of China(12426301)。
文摘This review presents battery design automation(BDA)as a transformative artificial intelligence(AI)-driven paradigm for the next-generation lithium-ion battery research and development.Addressing the intricacy of the problems and challenges in developing lithium-ion batteries with better performance,which are cross-scale,long-process,and multi-factor,BDA integrates multi-scale simulations and artificial intelligence into a unified platform.It ranges from atomic-scale material screening to system-level performance prediction.By bridging the gap between scientific innovation and industrial applications,BDA facilitates the development of lithium-ion battery,enhancing its efficiency,safety,and energy density.The paper outlines BDA's architecture,core technologies,current progress,and future challenges,highlighting its potential to revolutionize the battery design process and strengthen the pivotal role of lithium-ion battery in energy storage technology.
