Despite the widespread applications of machine learning force fields (MLFF) in solids and small molecules, there is a notable gap in applying MLFF to simulate liquid electrolyte, a critical component of the current commercial lithium-ion battery. In this work, we introduce BAMBOO (\textbf{B}yteDance \textbf{A}I \textbf{M}olecular Simulation \textbf{Boo}ster), a predictive framework for molecular dynamics (MD) simulations, with a demonstration of its capability in the context of liquid electrolyte for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from MD simulations. Finally, we propose a density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity across various solvents and salt combinations. The current model, trained on more than 15 chemical species, achieves the average density error of 0.01 g/cm$^3$ on various compositions compared with experiment.

翻译：尽管机器学习力场（MLFF）在固体和小分子中得到了广泛应用，但在将其应用于模拟液体电解质——当前商用锂离子电池的关键组件——方面仍存在显著空白。在本工作中，我们介绍了BAMBOO（\textbf{B}yteDance \textbf{A}I \textbf{M}olecular Simulation \textbf{Boo}ster），一个用于分子动力学（MD）模拟的预测性框架，并以锂电池液体电解质为背景展示了其能力。我们设计了一种受物理学启发的图等变Transformer架构作为BAMBOO的主干，以从量子力学模拟中学习。此外，我们引入了一种集成知识蒸馏方法，并将其应用于MLFF，以减少MD模拟中观测值的波动。最后，我们提出了一种密度对齐算法，以使BAMBOO与实验测量结果对齐。BAMBOO在预测各种溶剂和盐组合的关键电解质性质（如密度、粘度和离子电导率）方面展示了最先进的准确性。当前模型在超过15种化学物种上训练完成，与实验相比，在各种成分上的平均密度误差达到0.01 g/cm$^3$。