AI-assisted nanolayer-engineered solid-state electrolyte interfaces for lithium dendrite suppression in high-energy lithium metal batteries

  • Tanveer Ahmad Wani orcid

    Department of Physics, Noida International University, Greater Noida 203201, Uttar Pradesh, India
  • Ramesh Saini orcid

    Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
  • Shobhit Goyal orcid

    Quantum University Research Center, Quantum University, Roorkee 247667, Uttarakhand, India
  • Manni Sruthi orcid

    Centre for Multidisciplinary Research, Anurag University, Hyderabad 500088, Telangana, India
  • Chetansinh R. Vaghela orcid

    Faculty of Science, Gokul Global University, Siddhpur 384151, Gujarat, India
  • Yinxin Tang orcid

    Faculty of Education, Shinawatra University, Samkhok 12160, Pathum Thani, Thailand
  • Kaustubh Milind Utpat orcid

    Department of Mechanical Engineering, Vishwakarma University, Pune 411048, Maharashtra, India
Article ID: 4568
DOI: https://doi.org/10.59400/esc4568
Keywords: lithium metal batteries; solid-state electrolytes; nanolayer engineering; dendrite suppression; artificial intelligence; interfacial stability; high-energy-density batteries

Abstract

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 simulation results show that the optimized nanolayer-engineered interface gave an ionic conductivity of 1.21 × 10−3 S cm−1, which was an enhancement of 34.8% compared to the conventional interface. This framework helped reduce interfacial resistance to 46 Ω cm2 from 82 Ω cm2, which was an improvement of 43.9%, and increased the critical current density to 2.1 mA cm−2 from 1.3 mA cm−2. Additionally, the optimized interface retained 93.4% of its capacity after 500 cycles of charge-discharge, which is an improvement of 93.4% from the conventional interface’s 79.6%. 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.

Published
2026-11-06
How to Cite
Tanveer Ahmad Wani, Ramesh Saini, Shobhit Goyal, Manni Sruthi, Chetansinh R. Vaghela, Yinxin Tang, & Kaustubh Milind Utpat. (2026). AI-assisted nanolayer-engineered solid-state electrolyte interfaces for lithium dendrite suppression in high-energy lithium metal batteries. Energy Storage and Conversion, 4(2). https://doi.org/10.59400/esc4568
Issue
Vol. 4 No. 3 (2026): In Progress
Section
Article

Copyright (c) 2026 Tanveer Ahmad Wani, Ramesh Saini, Shobhit Goyal, Manni Sruthi, Chetansinh R. Vaghela, YINXIN TANG, Kaustubh Milind Utpat

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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