Li metal is considered an ideal anode material for application in the next-generation secondary batteries.However,the commercial application of Li metal batteries has not yet been achieved due to the safety concern ca...Li metal is considered an ideal anode material for application in the next-generation secondary batteries.However,the commercial application of Li metal batteries has not yet been achieved due to the safety concern caused by Li dendrites growth.Despite the fact that many recent experimental studies found that external pressure suppresses the Li dendrites growth,the mechanism of the external pressure effect on Li dendrites remains poorly understood on the atomic scale.Herein,the large-scale molecular dynamics simulations of Li dendrites growth under different external pressure were performed with a machine learning potential,which has the quantum-mechanical accuracy.The simulation results reveal that the external pressure promotes the process of Li self-healing.With the increase of external pressure,the hole defects and Li dendrites would gradually fuse and disappear.This work provides a new perspective for understanding the mechanism for the impact of external pressure on Li dendrites.展开更多
The Kohn-Sham density functional theory(KS-DFT)has played an important role in materials simulation for a long time.To better serve the industry,it is desirable to have an integrated solution that supports different c...The Kohn-Sham density functional theory(KS-DFT)has played an important role in materials simulation for a long time.To better serve the industry,it is desirable to have an integrated solution that supports different calculation tasks by KSDFT with different corrections and modifications.In this work,we present Hylanemos,a plane wave pseudopotential(PW-PP)KS-DFT package written entirely in the Julia programming language,which could offer such a solution.First,we analyze the code design to get the flexibility needed to implement such a solution.Then,we show that its accuracy and speed are comparable to widely-used packages.Next,we show its ability to perform common tasks such as single point(SP)calculations,geometry optimization,and transition state calculations.Finally,the LDA+Gutzwiller(LDA+G)method is presented,a feature not commonly found in DFT packages.In addition,we have also developed a set of ultrasoft(US)PP through parameter adjustment and optimization.This set of PP,called Eacomp PP,has a low cutoff energy(<18 Ha)and exhibits excellent performance in our benchmarks.Combining a performant package and optimized potentials will facilitate our in-depth efforts in promoting industrialization.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supported by the National Natural Science Foundation of China(No.52272180,No.12174162,No.51962010)the Shenzhen Science and Technology Research Grant(No.20220810123501001)the IER Foundation 2021(IERF202104)。
文摘Li metal is considered an ideal anode material for application in the next-generation secondary batteries.However,the commercial application of Li metal batteries has not yet been achieved due to the safety concern caused by Li dendrites growth.Despite the fact that many recent experimental studies found that external pressure suppresses the Li dendrites growth,the mechanism of the external pressure effect on Li dendrites remains poorly understood on the atomic scale.Herein,the large-scale molecular dynamics simulations of Li dendrites growth under different external pressure were performed with a machine learning potential,which has the quantum-mechanical accuracy.The simulation results reveal that the external pressure promotes the process of Li self-healing.With the increase of external pressure,the hole defects and Li dendrites would gradually fuse and disappear.This work provides a new perspective for understanding the mechanism for the impact of external pressure on Li dendrites.
基金supported by the National Natural Science Foundation of China(Grant No.12426301)。
文摘The Kohn-Sham density functional theory(KS-DFT)has played an important role in materials simulation for a long time.To better serve the industry,it is desirable to have an integrated solution that supports different calculation tasks by KSDFT with different corrections and modifications.In this work,we present Hylanemos,a plane wave pseudopotential(PW-PP)KS-DFT package written entirely in the Julia programming language,which could offer such a solution.First,we analyze the code design to get the flexibility needed to implement such a solution.Then,we show that its accuracy and speed are comparable to widely-used packages.Next,we show its ability to perform common tasks such as single point(SP)calculations,geometry optimization,and transition state calculations.Finally,the LDA+Gutzwiller(LDA+G)method is presented,a feature not commonly found in DFT packages.In addition,we have also developed a set of ultrasoft(US)PP through parameter adjustment and optimization.This set of PP,called Eacomp PP,has a low cutoff energy(<18 Ha)and exhibits excellent performance in our benchmarks.Combining a performant package and optimized potentials will facilitate our in-depth efforts in promoting industrialization.
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.
