The development of accurate and efficient interatomic potentials using machine learning has emerged as an important approach in materials simulations and discovery. However, the systematic construction of diverse, converged training sets remains challenging. We develop a deep learning-based interatomic potential for the Li₇La₃Zr₂O₁₂ (LLZO) system. Our interatomic potential is trained using a diverse dataset obtained from databases and first-principles simulations. We propose using the coverage of the training and test sets as the convergence criteria for the training iterations, where the coverage is calculated by principal component analysis. This results in an accurate LLZO interatomic potential that can describe the structure and dynamical properties of LLZO systems meanwhile greatly reducing computational costs compared to density functional theory calculations. The interatomic potential accurately describes radial distribution functions and thermal expansion coefficient consistent with experiments. It also predicts the tetragonal-to-cubic phase transition behaviors of LLZO systems. Our work provides an efficient training strategy to develop accurate deep-learning interatomic potential for complex solid-state electrolyte materials, providing a promising simulation tool to accelerate solid-state battery design and applications.

使用机器学习开发准确有效的原子间势已成为材料模拟和发现的重要方法。然而，系统构建多样化、融合的训练集仍然具有挑战性。_{我们为 Li 7} La ₃ Zr ₂ O ₁₂ (LLZO) 系统开发了基于深度学习的原子间势。我们的原子间势是使用从数据库和第一原理模拟获得的多样化数据集进行训练的。我们建议使用训练集和测试集的覆盖率作为训练迭代的收敛标准，其中覆盖率是通过主成分分析计算的。这产生了精确的 LLZO 原子间势，可以描述 LLZO 系统的结构和动力学性质，同时与密度泛函理论计算相比大大降低了计算成本。原子间势准确描述了径向分布函数和热膨胀系数，与实验一致。它还预测了 LLZO 系统的四方到立方相变行为。我们的工作提供了一种有效的培训策略，为复杂的固态电解质材料开发精确的深度学习原子间势，为加速固态电池的设计和应用提供了有前景的模拟工具。