arXiv：cs.LG· Jingxuan Ding, Laura Zichi, Matteo Carli, Menghang Wang, Albert Musaelian, Yu Xie, Boris Kozinsky·· 5 小时前AI 评分38
固态电池界面演化中的耦合反应与扩散：主动学习与等变神经网络势实现量子精度大规模反应模拟
Coupled reaction and diffusion governing interface evolution in solid-state batteries
AI 导读
研究团队利用主动学习和深度等变神经网络原子间势，对对称电池单元开展了量子精度的大规模显式反应模拟，并结合局部原子环境聚类无监督分类，自动表征界面处的耦合反应与互扩散。分析揭示SEI中形成了一种此前未被报道的晶态无序相 Li₂S₀.₇₂P₀.₁₄Cl₀.₁₄，该相无法由纯热力学预测获得。模拟结果与实验观测一致，并阐明了沿界面显著Li迁移所主导的Li蠕变机制。
正文
Abstract:Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid state batteries. However, challenges persist due to difficulties in experimentally characterizing buried interfaces and limits in simulation speed and accuracy. We conduct large-scale explicit reactive simulations with quantum accuracy for a symmetric battery cell, {\symcell}, enabled by active learning and deep equivariant neural network interatomic potentials. To automatically characterize the coupled reactions and interdiffusion at the interface, we formulate and use unsupervised classification techniques based on clustering in the space of local atomic environments. Our analysis reveals the formation of a previously unreported crystalline disordered phase, Li$_2$S$_{0.72}$P$_{0.14}$Cl$_{0.14}$, in the SEI, that evaded previous predictions based purely on thermodynamics, underscoring the importance of explicit modeling of full reaction and transport kinetics. Our simulations agree with and explain experimental observations of the SEI formations and elucidate the Li creep mechanisms, critical to dendrite initiation, characterized by significant Li motion along the interface. Our approach is to crease a digital twin from first principles, without adjustable parameters fitted to experiment. As such, it offers capabilities to gain insights into atomistic dynamics governing complex heterogeneous processes in solid-state synthesis and electrochemistry.
| Comments: | Supplementary Information is available as an ancillary file |
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2506.10944 [cond-mat.mtrl-sci] |
| (or arXiv:2506.10944v2 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2506.10944 arXiv-issued DOI via DataCite |
Submission history
From: Matteo Carli [view email]
[v1]
Thu, 12 Jun 2025 17:49:05 UTC (36,154 KB)
[v2]
Fri, 2 Oct 2026 10:28:59 UTC (47,585 KB)
来源：arXiv：cs.LG · arxiv.org