Li$_xTM$O$_2$ (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability as a function of Li content at the atomistic scale. Here, we use Li$_x$CoO$_2$ (LCO) as a model system to benchmark a scale-bridging framework that combines density functional theory (DFT) calculations at the atomistic scale with phase field modeling at the continuum scale to understand the impact of phase stability on microstructure evolution. This scale bridging is accomplished by incorporating traditional statistical mechanics methods with integrable deep neural networks, which allows formation energies for specific atomic configurations to be coarse-grained and incorporated in a neural network description of the free energy of the material. The resulting realistic free energy functions enable atomistically informed phase-field simulations. These computational results allow us to make connections to experimental work on LCO cathode degradation as a function of temperature, morphology and particle size.

翻译：Li$_xTM$O$_2$(TM ⁇ Ni, Co, Mn})是利离电池有前途的催化器,其电化学循环性能受到晶体结构和阶段稳定性的强烈制约,这是在原子规模上李内容的函数。在这里,我们使用Li$_x$CoO$_2美元(LCO)作为模型系统,以基准标定一个缩放框架,将原子密度功能理论(DFT)的计算与连续规模的分阶段实地模型(DFT)的计算结合起来,以了解阶段稳定性对微结构演变的影响。通过将传统的统计力学方法与可耐受的深神经网络结合,使特定原子配置的形成能量能够分解并纳入材料自由能量的神经网络描述中。由此产生的现实自由能源功能可以使原子知情的阶段模拟得以进行。这些计算结果使我们能够将LCO阴道退化作为温度、形态学和颗粒大小的函数与实验性工作联系起来。