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Machine Learning Enhanced Electrochemical Simulations for Dendrites Nucleation in Li Metal Battery

Autor: Hu, Taiping, Huang, Haichao, Zhou, Guobing, Wang, Xinyan, Zhu, Jiaxin, Cheng, Zheng, Fu, Fangjia, Wang, Xiaoxu, Dai, Fuzhi, Yu, Kuang, Xu, Shenzhen

Uncontrollable dendrites growth during electrochemical cycles leads to low Coulombic efficiency and critical safety issues in Li metal batteries. Hence, a comprehensive understanding of the dendrite formation mechanism is essential for further enhanc

Externí odkaz: http://arxiv.org/abs/2406.14025

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A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Autor: Hu, Taiping, Yang, Teng, Liu, Jianchuan, Deng, Bin, Huang, Zhengtao, Wang, Xiaoxu, Dai, Fuzhi, Zhou, Guobing, Fu, Fangjia, Tuo, Ping, Xu, Ben, Xu, Shenzhen

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challeng

Externí odkaz: http://arxiv.org/abs/2309.01146

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A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment

Autor: Wang, XiaoYang, Wang, YiNan, Zhang, LinFeng, Dai, FuZhi, Wang, Han

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of tungsten under h

Externí odkaz: http://arxiv.org/abs/2111.04281

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