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<p>Despite the widespread applications of machine learning force fields (MLFFs) in solids and small molecules, there is a notable gap in applying MLFFs to simulate liquid electrolytes—a critical component of current commercial lithium-ion batteries. Here we introduce ByteDance Artificial intelligence Molecular simulation Booster (BAMBOO), a predictive framework for molecular dynamics simulations, with a demonstration of its capability 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 introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from molecular dynamics 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 an average density error of 0.01 g cm<sup>−</sup><sup>3</sup> on various compositions compared with experiment.</p>

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A predictive machine learning force-field framework for liquid electrolyte development

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Sheng Gong,
  • Yumin Zhang,
  • Zhenliang Mu,
  • Zhichen Pu,
  • Hongyi Wang,
  • Xu Han,
  • Zhiao Yu,
  • Mengyi Chen,
  • Tianze Zheng,
  • Zhi Wang,
  • Lifei Chen,
  • Zhenze Yang,
  • Xiaojie Wu,
  • Shaochen Shi,
  • Weihao Gao,
  • Wen Yan,
  • Liang Xiang

摘要

Despite the widespread applications of machine learning force fields (MLFFs) in solids and small molecules, there is a notable gap in applying MLFFs to simulate liquid electrolytes—a critical component of current commercial lithium-ion batteries. Here we introduce ByteDance Artificial intelligence Molecular simulation Booster (BAMBOO), a predictive framework for molecular dynamics simulations, with a demonstration of its capability 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 introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from molecular dynamics 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 an average density error of 0.01 g cm−3 on various compositions compared with experiment.