BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development

• URL: http://arxiv.org/abs/2404.07181v4
• Date: Mon, 22 Apr 2024 17:44:12 GMT
• Title: BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
• Authors: Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang,
• Abstract summary: We introduce BAMBOO, a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. BamBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity. We envision this work as paving the way to a "universal MLFF" capable of simulating properties of common organic liquids.
• Score: 11.682763325188525
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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