Predicting Reactivity and Passivation of Solid-State Battery Interfaces

In this work, we build a computationally inexpensive,
data-driven
model that utilizes atomistic structure information to predict the
reactivity of interfaces between any candidate solid-state electrolyte
material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time
evolution of the solid electrolyte–Li metal interfaces for
67 different materials. Predicting the reactivity of solid-state interfaces
with ab initio techniques remains an elusive challenge
in materials discovery and informatics, and previous work on predicting
interfacial compatibility of solid-state Li-ion electrolytes and Li
metal anodes has focused mainly on thermodynamic convex hull calculations.
Our framework involves training machine learning models on AIMD data,
thereby capturing information on both kinetics and thermodynamics,
and then leveraging these models to predict the reactivity of thousands
of new candidates in the span of seconds, avoiding the need for additional
weeks-long AIMD simulations. We identify over 300 new chemically stable
and over 780 passivating solid electrolytes that are predicted to
be thermodynamically unfavored. Our results indicate many potential
solid-state electrolyte candidates have been incorrectly labeled unstable
via purely thermodynamic approaches using density functional theory
(DFT) energetics, and that the pool of promising, Li-stable solid-state
electrolyte materials may be much larger than previously thought from
screening efforts. To showcase the value of our approach, we highlight
two borate materials that were identified by our model and confirmed
by further AIMD calculations to likely be highly conductive and chemically
stable with Li: LiB13C2 and LiB12PC.

本研究构建了一种计算成本低廉的数据驱动模型，该模型借助原子级结构信息，可预测任意候选固态电解质材料与金属锂负极之间的界面反应活性。该模型基于67种不同材料的固态电解质-金属锂界面时间演化的从头算分子动力学（ab initio molecular dynamics, AIMD）模拟数据进行训练。采用从头算技术预测固态界面反应活性，仍是材料发现与信息学领域中尚未攻克的难题；此前针对固态锂离子电解质与金属锂负极界面兼容性的预测研究，主要集中于热力学凸包计算。本研究框架依托AIMD数据训练机器学习模型，从而同时捕获动力学与热力学信息；随后可借助这些模型在数秒内预测数千种新候选材料的界面反应活性，无需再进行耗时数周的AIMD模拟。本研究筛选出300余种全新的化学稳定固态电解质，以及780余种经预测热力学上非稳定的钝化型固态电解质。研究结果表明，诸多潜在固态电解质候选材料曾被仅基于密度泛函理论（density functional theory, DFT）能量的纯热力学方法错误标记为不稳定；而具备应用前景、可与锂稳定共存的固态电解质材料池，或许比此前筛选工作所得认知要大得多。为展示本方法的价值，我们重点介绍了两种由本模型筛选出、并经后续AIMD计算证实可与锂形成高导电且化学稳定界面的硼酸盐材料：LiB₁₃C₂与LiB₁₂PC。