Phonon-Driven Limitations of AI-Predicted Stability in Li-Ion Anode Materials: A Computational Meta-Analysis of Vibrational Instabilities

Authors

  • Hafsat Abubakar Garba Department of Chemistry (Material Science and Explosives), Nigerian Defence Academy Postgraduate School, Ribadu Campus, Kaduna, Nigeria.
  • Abubakar Habib Idris
  • Hannatu Akanang
  • Olumuyiwa Oyekunle Akintola
  • John Dedah
  • Warji Muhammad Ibrahim
  • Jamila Ibrahim Shekarau

DOI:

https://doi.org/10.33003/fjs-2026-1015-5931

Keywords:

Phonon Instability, Li-Ion Anodes, Machine Learning Interatomic Potentials, Vibrational Analysis, Silicon Anode, Carbon Anode

Abstract

Machine learning interatomic potentials (MLIPs) and high-throughput density functional theory (DFT) screening have dramatically accelerated the discovery of Li-ion battery anode materials; however, a critical gap persists between computational stability predictions and real-world synthesis feasibility. This study presents a systematic computational meta-analysis of phonon-driven instability among 79 Li-ion anode candidate structures spanning silicon-based (n = 34), carbon-based (n = 21), and lithium-metal phases (n = 24), all predicted to be stable by the Materials Project AI-assisted DFT pipeline (energy above hull, Ehull < 0.05 eV atom⁻¹). Using the finite-displacement method implemented in Phonopy and an empirical Born–von Karman force-constant model sampled at four high-symmetry Brillouin-zone points, 49 of the 79 structures (62.0%) were classified as dynamically unstable within the adopted phonon-screening framework. Carbon-based anodes exhibited the highest failure rate (95.2%), followed by lithium-metal phases (70.8%) and silicon-based anodes (35.3%). Instabilities were overwhelmingly concentrated at the Gamma point (98.0% of unstable structures), identifying the zone center as the predominant location of phonon instabilities across all three anode families. Chi-squared analysis confirmed that failure rates differed significantly among material families (χ² = 20.94, df = 2, p < 0.0001). These findings highlight important limitations in current AI-based screening frameworks for anode materials and support the incorporation of phonon instability screening as an additional post-processing step in high-throughput materials-discovery workflows.

References

Ahuome, B. A., Aungwa, F., Adamu, M. A., & Ashezua, J. A. (2023). Computation of the cohesive energies of some selected ionic liquid crystals (NaCl and LiCl) using density functional theory FHI-AIMS code. FUDMA Journal of Sciences, 3(2), 15–28. https://doi.org/10.33003/fjs-2019-0302-1477

Born, M., & Huang, K. (1954). Dynamical theory of crystal lattices. Clarendon Press.

Di Leo, C. V., Rejovitzky, E., & Anand, L. (2015). Diffusion–deformation theory for amorphous silicon anodes: The role of plastic deformation on electrochemical performance. International Journal of Solids and Structures, 67–68, 283–296. https://doi.org/10.1016/j.ijsolstr.2015.04.028

Goodenough, J. B., & Park, K.-S. (2013). The Li-ion rechargeable battery: A perspective. Journal of the American Chemical Society, 135(4), 1167–1176. https://doi.org/10.1021/ja3091438

Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010). A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. The Journal of Chemical Physics, 132(15), 154104. https://doi.org/10.1063/1.3382344

Isbill, S. B., Shields, A. E., Brubaker, Z. E., Miskowiec, A. J., & Niedziela, J. L. (2024). Strain-induced shifts in defective graphite phonon modes predicted by density functional theory. The Journal of Physical Chemistry C, 128(39), 16662–16671. https://doi.org/10.1021/acs.jpcc.4c03191

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. https://doi.org/10.1063/1.4812323

Kirklin, S., Meredig, B., & Wolverton, C. (2013). High-throughput computational screening of new Li-ion battery anode materials. Advanced Energy Materials, 3(7), 833–838. https://doi.org/10.1002/aenm.201200593

Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023). Scaling deep learning for materials discovery. Nature, 624(7990), 80–85. https://doi.org/10.1038/s41586-023-06735-9

Muy, S., Voss, J., Schlem, R., Koerver, R., Sedlmaier, S. J., Maglia, F., Lamp, P., Zeier, W. G., & Shao-Horn, Y. (2019). High-throughput screening of solid-state Li-ion conductors using lattice-dynamics descriptors. iScience, 16, 270–282. https://doi.org/10.1016/j.isci.2019.05.036

Nwachuku, D. N. (2025). First-principles investigation of the electronic structure and optical response of Ga₂S₃ and Ga₂Se₃ chalcogenides. FUDMA Journal of Sciences, 9(11), 27–31. https://doi.org/10.33003/fjs-2025-0911-4018

Togo, A., & Tanaka, I. (2015). First principles phonon calculations in materials science. Scripta Materialia, 108, 1–5. https://doi.org/10.1016/j.scriptamat.2015.07.021

Yu, S., Kim, S.-O., Kim, H.-S., & Choi, W. (2019a). Computational screening of anode materials for sodium-ion batteries. Journal of The Electrochemical Society, 166(10), A1915–A1923. https://doi.org/10.1149/2.0691910jes

Yu, S., Kim, S.-O., Kim, H.-S. & Choi, W. (2019b). Computational screening of anode materials for potassium-ion batteries. International Journal of Energy Research, 43(13), 7192–7205. https://doi.org/10.1002/er.4771

Composition of the Final Li-ion Anode Candidate Dataset by Material Family

Downloads

Published

10-09-2026

How to Cite

Garba, H. A., Idris, A. H., Akanang, H., Akintola, O. O., Dedah, J., Ibrahim, W. M., & Shekarau, J. I. (2026). Phonon-Driven Limitations of AI-Predicted Stability in Li-Ion Anode Materials: A Computational Meta-Analysis of Vibrational Instabilities. FUDMA Journal of Sciences, 10(15), 221-225. https://doi.org/10.33003/fjs-2026-1015-5931