Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes 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 pioneer an ensemble knowledge distillation approach and apply it on MLFFs to improve the stability of MD simulations. Finally, we propose the 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. Our 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 experimental data. Moreover, our model demonstrates transferability to molecules not included in the quantum mechanical dataset. We envision this work as paving the way to a "universal MLFF" capable of simulating properties of common organic liquids.

翻译：尽管机器学习力场（MLFF）在固体和小分子领域已有广泛应用，但在复杂液态电解质中的应用仍存在显著空白。本研究提出BAMBOO（字节跳动人工智能分子模拟助推器），一种用于分子动力学（MD）模拟的新型框架，并展示其在锂电池液态电解质场景中的能力。我们以物理启发的图等变Transformer架构作为BAMBOO的核心，从量子力学模拟中学习。此外，我们首创性地将集成知识蒸馏方法应用于MLFF，以提升MD模拟的稳定性。最后，我们提出密度对齐算法，使BAMBOO与实验测量结果相校准。在预测不同溶剂与盐组合的关键电解质性质（如密度、粘度、离子电导率）时，BAMBOO展现出最优精度。当前模型基于超过15种化学物种训练，在不同组合物的密度预测中与实验数据的平均误差达0.01 g/cm$^3$。此外，模型展现出对未包含在量子力学数据集中分子的可迁移性。我们展望该工作将为能够模拟常见有机液体性质的"通用MLFF"铺平道路。