AI-assisted nanolayer-engineered solid-state electrolyte interfaces for lithium dendrite suppression in high-energy lithium metal batteries
Commercialization of lithium metal batteries with high energy density is limited largely due to uncontrolled lithium dendrite growth, poor interfacial resistance of solid-state electrolytes, and inadequate cycle stability of solid-state electrolytes. A new architecture that leverages artificial intelligence to design engineered nanolayer interfaces for solid-state electrolytes enables controlled lithium-ion transport and prevents dendrite propagation in next-generation batteries. To achieve this, the proposed framework combines Multiphysics interfacial modeling with an artificial intelligence optimization module to simultaneously determine the optimal thickness and interfacial parameters for nanolayers to optimize ionic conductivity while minimizing electrochemical degradation. Simulation results show that the engineered interface provides a significantly better electrochemical performance than any existing solid-state electrolyte configuration. The engineered interface provides an ionic conductivity of 1.21 × 10−3 S·cm−1, an improvement of approximately 34.8%, and a decrease in interfacial resistance of 82 Ω·cm² to 46 Ω·cm², a decrease of 43.9%, and a critical current density of 2.1 mA·cm−2 compared to an average critical current density of 1.3 mA·cm−2 demonstrating greater tolerance to dendrites. Long-term cycle testing indicates that the proposed configuration retains 93.4% capacity after 500 cycles, while the conventional interface retains only 79.6% of its capacity when cycled under equivalent conditions. Collectively, these results indicate that the combination of nanolayer engineering and artificial intelligence optimization enables uniform deposition of lithium ions, enhances interfacial stability, and reduces the degree of dendrite-induced degradation. The proposed framework enables large-scale development of safer, long-lasting solid-state lithium-ion batteries and facilitates high-energy-density storage for electric vehicles and large renewable energy systems
Authors
- Tanveer Ahmad Wani (ORCID: https://orcid.org/0000-0001-5582-6190)
- Chetansinh R. Vaghela (ORCID: https://orcid.org/0000-0001-5692-5190)
- Manni Sruthi
- Kaustubh Milind Utpat
- Ramesh Saini
- YINXIN TANG
- Shobhit Goyal
Institutions
- Shinawatra University (TH)
- Noida International University (IN)
- Advanced Numerical Research and Analysis Group (IN)
- Vishwakarma University (IN)
- Quantum University (IN)
- Chitkara University (IN)
Publication Details
- Journal
- Energy Storage and Conversion
- Published
- 2026-11-06
- DOI
- https://doi.org/10.59400/esc4568
- Primary Topic
- Advanced Battery Materials and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00