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-GPUMD is an efficient domestic molecular dynamics simulation software developed and maintained by Professor Fan Zheyong from Bohai University. The software first released its public version 1.0 in 2017 [Computer Physics Communications 218, 10 (2017)] and has currently been iterated to version 3.9.4. GPUMD includes both commonly used empirical potentials and NEP (Neuroevolution Potential) machine learning potentials. Up to now, GPUMD has been used by thousands of users in many countries around the world and has attracted dozens of researchers to participate in its development. It is widely applied in fields such as heat and mass transfer, mechanical properties, structural phase transitions, irradiation damage, spectroscopy, and catalysis. Related achievements have been published in top academic journals such as Nature, Nature Communications, J. Am. Chem. Soc, ACS Nano, Phys. Rev. Lett, J. Mech. Phys. Solids, J. Chem. Theory Comput., Phys. Rev. B, and J. Chem. Phys.
+GPUMD is an efficient domestic molecular dynamics simulation software developed and maintained by Professor Zheyong Fan from Bohai University. The software first released its public version 1.0 in 2017 [Computer Physics Communications 218, 10 (2017)] and has currently been iterated to version 3.9.4. GPUMD includes both commonly used empirical potentials and NEP (Neuroevolution Potential) machine learning potentials. Up to now, GPUMD has been used by thousands of users in many countries around the world and has attracted dozens of researchers to participate in its development. It is widely applied in fields such as heat and mass transfer, mechanical properties, structural phase transitions, irradiation damage, spectroscopy, and catalysis. Related achievements have been published in top academic journals such as Nature, Nature Communications, J. Am. Chem. Soc, ACS Nano, Phys. Rev. Lett, J. Mech. Phys. Solids, J. Chem. Theory Comput., Phys. Rev. B, and J. Chem. Phys.
 
 In June 2024, GPUMD&NEP joined the DeepModeling community. As an innovative and highly efficient MD simulation and machine learning potential function tool, it further provides support for the Materials Genome Project and the AI4S community.
 
 ## Introduction
 
 In recent years, all-solid-state lithium-ion batteries have attracted much attention due to their high safety and high energy density. As a solid-state electrolyte material with high ionic conductivity and stability, Li7La3Zr2O12 (LLZO) is particularly remarkable. However, there is a significant difference between the theoretically predicted activation energy (about 1.2 eV) and the experimentally measured value (about 0.45 eV) for lithium-ion migration in the tetragonal phase LLZO. This contradiction limits the in-depth understanding and optimization of the material's performance.
 
-Recently, Professor Zhu Yizhou's team from Westlake University successfully solved this problem by using GPUMD&NEP and achieved high-precision and large-scale simulations of lithium-ion migration in LLZO. The research accurately predicted key properties such as the temperature of the tetragonal-cubic phase transition, lattice parameters, ionic conductivity, activation energy for lithium-ion migration, and defect concentrations of lithium and oxygen in LLZO from the theoretical level, and these results are almost completely consistent with the experimentally measured values. Notably, this is also the first application of GPUMD&NEP in the field of solid-state electrolytes, demonstrating its strong potential in studying large-scale complex material systems.
+Recently, Professor Yizhou Zhu's team from Westlake University successfully solved this problem by using GPUMD&NEP and achieved high-precision and large-scale simulations of lithium-ion migration in LLZO. The research accurately predicted key properties such as the temperature of the tetragonal-cubic phase transition, lattice parameters, ionic conductivity, activation energy for lithium-ion migration, and defect concentrations of lithium and oxygen in LLZO from the theoretical level, and these results are almost completely consistent with the experimentally measured values. Notably, this is also the first application of GPUMD&NEP in the field of solid-state electrolytes, demonstrating its strong potential in studying large-scale complex material systems.
 
 ## GPUMD&NEP: A Perfect Combination of High Efficiency and High Precision