Schrödinger, Inc., New York, New York 10036, United States.
Schrödinger, Inc., Portland, Oregon 97204, United States.
J Phys Chem B. 2022 Aug 25;126(33):6271-6280. doi: 10.1021/acs.jpcb.2c03746. Epub 2022 Aug 16.
Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity and ionic diffusivity) from first principles necessary to support improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations, which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training data set is composed solely of non-periodic density functional theory (DFT), allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD), which would be prohibitively expensive for generating large data sets with periodic DFT. In this report, we focus on seven common carbonates and LiPF, but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large-scale atomistic modeling of many important battery chemistries.
液体电解质是锂离子电池的最重要组成部分之一，而锂离子电池是现代世界的关键技术。然而，我们仍然缺乏计算工具，无法从第一性原理准确计算这些材料（粘度和离子扩散率）的关键性质，这对于支持改进的设计是必要的。在这项工作中，我们报告了一种用于液体电解质模拟的基于机器学习的力场，它弥合了分离范围杂化密度泛函理论的准确性和经典力场的效率之间的差距。与实验数据相比，该力场对材料性质的预测具有定量准确性。我们的模型使用 QRNN 深度神经网络架构，其中包括远程相互作用和全局电荷平衡。训练数据集仅由非周期性密度泛函理论 (DFT) 组成，允许实际使用准确的理论（这里是 ωB97X-D3BJ/def2-TZVPD），而使用周期性 DFT 生成大型数据集的成本过高。在本报告中，我们专注于七种常见的碳酸盐和 LiPF，但这种方法几乎没有假设，可以很容易地应用于任何液体电解质系统。这为许多重要电池化学物质的大规模原子建模提供了一个有前途的途径。
