A deep learning-based computational method is proposed for soft matter dynamics-the deep Onsager-Machlup method(DOMM).It combines the brute forces of deep neural networks(DNNs)with the fundamental physics principle-On...A deep learning-based computational method is proposed for soft matter dynamics-the deep Onsager-Machlup method(DOMM).It combines the brute forces of deep neural networks(DNNs)with the fundamental physics principle-OnsagerMachlup variational principle(OMVP).In the DOMM,the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions.It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions.This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables,multiple scales,and multiple dissipative processes.Actually,the DOMM can be regarded as an extension of the deep Ritz method(DRM)developed by E and Yu that uses DNNs to solve static problems in physics.In this work,as the first step,we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems:particle diffusion in dilute solutions,and two-phase dynamics with and without hydrodynamics.The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods.These results show the accuracy and convergence of DOoMM and justify it as an alternative computational method for solving soft matter dynamics.展开更多
As a cathode material for potassium-ion batteries (PIBs), manganese-based layered oxides have attracted widespread attention due to their low cost, ease of synthesis, and high performance. However, the Jahn-Teller eff...As a cathode material for potassium-ion batteries (PIBs), manganese-based layered oxides have attracted widespread attention due to their low cost, ease of synthesis, and high performance. However, the Jahn-Teller effect caused by Mn3+ and the irreversible phase transformation of the structure leads to poor cycle stability, limiting the development of layered oxides in PIBs. Herein, we demonstrate the use of phase-transition-free CaTiO_(3) as rivets in K_(0.5)Mn_(0.9)Ti_(0.1)O_(2) by a simple solid-state method. As verified by the in situ X-ray diffraction, the CaTiO_(3) rivets effectively prevent the slippage of the transition metal layer during charge and discharge, inhibiting structural degradation. As a result, the obtained K_(0.5)Mn_(0.9)Ti_(0.1)O_(2)-0.02CaTiO_(3) shows excellent cycling stability and rate performance, with high capacities of 119.3 and 70.1 mAh·g^(-1) at 20 and 1000 mA·g^(-1), respectively. At 200 mA·g^(-1), the capacity retention remains 94.7% after more than 300 cycles. This work represents a new avenue for designing and optimizing layered cathode materials for PIBs and other batteries.展开更多
基金Xu is supported in part by National Natural Science Foundation of China(NSFC,No.12374209,No.12004082,No.12131010)D.Wang is partially supported by National Natural Science Foundation of China(Grant No.12101524,12422116)+3 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012199)Shenzhen Science and Technology Innovation Program(Grant No.JCYJ20220530143803007,RCYX20221008092843046,GXWD20201231105722002-20200829162111001)Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence(2023B1212010001)Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone Project(No.HZQSWS-KCCYB-2024016).
文摘A deep learning-based computational method is proposed for soft matter dynamics-the deep Onsager-Machlup method(DOMM).It combines the brute forces of deep neural networks(DNNs)with the fundamental physics principle-OnsagerMachlup variational principle(OMVP).In the DOMM,the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions.It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions.This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables,multiple scales,and multiple dissipative processes.Actually,the DOMM can be regarded as an extension of the deep Ritz method(DRM)developed by E and Yu that uses DNNs to solve static problems in physics.In this work,as the first step,we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems:particle diffusion in dilute solutions,and two-phase dynamics with and without hydrodynamics.The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods.These results show the accuracy and convergence of DOoMM and justify it as an alternative computational method for solving soft matter dynamics.
基金financially supported by two grants from the National Natural Science Foundation of China(Nos.U20A20247 and 51922038 to B.L.)The National Key Research and Development Program of Ministry of Science and Technology(No.2022YFA1402504)+2 种基金Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion(No.MATEC2023KF002)Guangdong Science and Technology Department(No.STKJ2021016)A.M.R acknowledges financial support through the Robert A.Bowen Endowed Professorship funds at Clemson University.
文摘As a cathode material for potassium-ion batteries (PIBs), manganese-based layered oxides have attracted widespread attention due to their low cost, ease of synthesis, and high performance. However, the Jahn-Teller effect caused by Mn3+ and the irreversible phase transformation of the structure leads to poor cycle stability, limiting the development of layered oxides in PIBs. Herein, we demonstrate the use of phase-transition-free CaTiO_(3) as rivets in K_(0.5)Mn_(0.9)Ti_(0.1)O_(2) by a simple solid-state method. As verified by the in situ X-ray diffraction, the CaTiO_(3) rivets effectively prevent the slippage of the transition metal layer during charge and discharge, inhibiting structural degradation. As a result, the obtained K_(0.5)Mn_(0.9)Ti_(0.1)O_(2)-0.02CaTiO_(3) shows excellent cycling stability and rate performance, with high capacities of 119.3 and 70.1 mAh·g^(-1) at 20 and 1000 mA·g^(-1), respectively. At 200 mA·g^(-1), the capacity retention remains 94.7% after more than 300 cycles. This work represents a new avenue for designing and optimizing layered cathode materials for PIBs and other batteries.
