  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.
