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
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
Li metal is acknowledged as an ultimate anode material for high-specific-energy batteries,although its safety and practical cyclability heavily depend on the mysterious interface between Li metal and liquid electrolyt...Li metal is acknowledged as an ultimate anode material for high-specific-energy batteries,although its safety and practical cyclability heavily depend on the mysterious interface between Li metal and liquid electrolyte(LLI).However,there are substantial gaps in understanding the multiple intertwined chemical and electrochemical processes occurring on the LLI.Here,we unprecedentedly present the disentangled analyses of these processes and correlate them with Li dendrite growth by multi-scale simulation techniques combining machine-learning-driven molecular dynamics and phase-field modeling.Our simulations demonstrate a close relationship between Li dendrite growth and the interface reactions,which can be attributed to the charge transfer process.We further reveal that the behaviors of bond cleavages can be regulated by varying charge distribution at the interface.We propose that the charge transfer kinetics,revealed by the newly developed formulism of machine learning potential incorporating charge information,can act as a descriptor to explain the driving forces behind these behaviors on the LLI.This work enables new opportunities to fundamentally understand the intertwined processes occurring on the LLI and provide crucial new insights into the electrode-electrolyte interface design for next-generation high-specific-energy batteries.展开更多
Although the electrode-electrolyte interface is a crucial electrochemical region,the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools.Theoretical simulat...Although the electrode-electrolyte interface is a crucial electrochemical region,the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools.Theoretical simulations with this delicate interface also remain one of the most significant challenges for atomistic modeling,particularly for the stable long-timescale simulation of the interface.Here we introduce a novel scheme,hybrid ab initio molecular dynamics combined with machine learning potential(HAML),to accelerate the modeling of electrode-electrolyte interface reactions.We demonstrate its effectiveness in modeling the interfaces of Li metal with both liquid and solid-state electrolytes,capturing critical processes over extended time scales.Furthermore,we reveal the role of interface reaction kinetics in interface regulation through HAML simulations,combined with the similarity analysis method.It is demonstrated that element(Se,F,O)doping in the Li_(6)PS_(5)Cl system is an effective strategy for enhancing interface reaction kinetics,facilitating the formation of a more stable interface protective layer faster at room temperature.Moreover,moderate structural instability can positively contribute to interface stabilization.HAML offers a promising approach for addressing the challenge of designing stable interfaces while reducing computational costs.This work provides valuable insights for advancing the understanding and optimization of interface behaviors in Li metal batteries.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
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 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.
基金supported by the National Natural Science Foundation of China(No.12426301)Shenzhen Science and Technology Research Grant(No.20231117083459001)AI for Science(AI4S)-Preferred Program,Peking University,Shenzhen,China.
文摘Li metal is acknowledged as an ultimate anode material for high-specific-energy batteries,although its safety and practical cyclability heavily depend on the mysterious interface between Li metal and liquid electrolyte(LLI).However,there are substantial gaps in understanding the multiple intertwined chemical and electrochemical processes occurring on the LLI.Here,we unprecedentedly present the disentangled analyses of these processes and correlate them with Li dendrite growth by multi-scale simulation techniques combining machine-learning-driven molecular dynamics and phase-field modeling.Our simulations demonstrate a close relationship between Li dendrite growth and the interface reactions,which can be attributed to the charge transfer process.We further reveal that the behaviors of bond cleavages can be regulated by varying charge distribution at the interface.We propose that the charge transfer kinetics,revealed by the newly developed formulism of machine learning potential incorporating charge information,can act as a descriptor to explain the driving forces behind these behaviors on the LLI.This work enables new opportunities to fundamentally understand the intertwined processes occurring on the LLI and provide crucial new insights into the electrode-electrolyte interface design for next-generation high-specific-energy batteries.
基金supported by the National Natural Science Foundation of China(No.12426301)Shenzhen Science and Technology Research Grant(No.20231117083459001)AI for Science(AI4S)-Preferred Program,Peking University,Shenzhen,China.
文摘Although the electrode-electrolyte interface is a crucial electrochemical region,the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools.Theoretical simulations with this delicate interface also remain one of the most significant challenges for atomistic modeling,particularly for the stable long-timescale simulation of the interface.Here we introduce a novel scheme,hybrid ab initio molecular dynamics combined with machine learning potential(HAML),to accelerate the modeling of electrode-electrolyte interface reactions.We demonstrate its effectiveness in modeling the interfaces of Li metal with both liquid and solid-state electrolytes,capturing critical processes over extended time scales.Furthermore,we reveal the role of interface reaction kinetics in interface regulation through HAML simulations,combined with the similarity analysis method.It is demonstrated that element(Se,F,O)doping in the Li_(6)PS_(5)Cl system is an effective strategy for enhancing interface reaction kinetics,facilitating the formation of a more stable interface protective layer faster at room temperature.Moreover,moderate structural instability can positively contribute to interface stabilization.HAML offers a promising approach for addressing the challenge of designing stable interfaces while reducing computational costs.This work provides valuable insights for advancing the understanding and optimization of interface behaviors in Li metal batteries.
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.
