fig6

Figure 6. MLIPs for SE. (A) Training workflow and convergence evaluation of the LLZO deep-learning interatomic potential using principal component analysis for dataset coverage. Reproduced with permission from ref.^[96]. Copyright 2024, Springer Nature; (B) Behler-Parrinello NNPs for Li₆PS₅Cl reproduce DFT accuracy and enable long MD simulations that capture disorder-enhanced Li⁺ diffusion. Reproduced with permission from ref.^[97]. Copyright 2024, American Chemical Society; (C) MSDs comparison between MLIPs and DFT at 800 K for 18 well-known Li⁺ conductors. Reproduced with permission from ref.^[98]. Copyright 2024, Royal Society of Chemistry; (D) Arrhenius plot of LiMgPO₄, LiTiPO_5, and their doped structures simulated by fine-tuned CHGNet-MD. Reproduced with permission from ref.^[99]. Copyright 2025, Royal Society of Chemistry; (E) Violin plots of prediction errors for seven uMLIPs with and without transition metals benchmarked against DFT, Reproduced with permission from ref.^[100]. Copyright 2025, Springer Nature; (F) Benchmarking and generalization of MLIPs across multiple models. Reproduced with permission from ref.^[101]. Copyright 2025, American Chemical Society; (G) Fine-tuning and generalization of MACE-based MLIPs for activation energy and conductivity of Na₃Zr₂Si₂PO₁₂ at 300 °C. Reproduced with permission from ref.^[102]. Copyright 2025, Royal Society of Chemistry. MD: Molecular dynamics; DFT: density functional theory; NNP: neural network potential; MSD: mean squared displacement; GNN: graph neural network; MACE: message passing atomic cluster expansion; SevenNet: scalable equivariance-enabled neural network; CHGNet: crystal hamiltonian graph neural network; M3GNet: materials GNN with 3-body interactions; GRACE: graph atomic cluster expansion; MP: Material Project; ORB: a family of uMLIPs for atomistic modelling of materials; SE: solid electrolyte; TM: transition metal; MLIP: machine-learning interatomic potential; SE: solid electrolyte; LLZO: Li₇La₃Zr₂O₁₂; uMLIP: universal MLIP.