Machine learning interatomic potentials for NaPSO glasses: the critical role of training data

The performance of five machine learning interatomic potentials (MLIPs), based on MACE, DeePMD, and GRACE-FS architectures, is assessed in reproducing the structural and mechanical properties of Na4P2S7-xOx​ mixed oxy-thiophosphate glasses, promising candidates for next-generation all-solid-state sodium batteries. The glass series (0 ≤ x ≤ 3.5) was chosen to explore the effect of oxygen incorporation on short- and medium-range structural order (SRO and MRO), a particularly challenging task as experimental data show non-linear trends in density, conductivity, and structural units with composition. Universal MLIPs, trained on generic databases (MP0, MATPES-r2SCAN, METPES-PBE) or on datasets comprising only the elements relevant to glassy solid electrolytes (GRACE-GSE), provide stable molecular dynamics but often predict artifacts such as edge-sharing tetrahedra or P–P chains, and fail to reproduce the SRO evolution. A DeePMD MLIP, trained via concurrent learning on crystalline Na–P–S–O structures without stoichiometry-specific data, exhibits similar limitations. Among the universal MLIPs, MATPES-r2SCAN reproduces density, mechanical properties, and anion (S and O) speciation with reasonable accuracy. Fine-tuning MP0 on the specific glass compositions (FT-MP0) significantly improves structural reproduction, accurately capturing density trends, mechanical properties, and the preference of oxygen for bridging positions. Nonetheless, even FT-MP0 cannot accurately reproduce the disproportionation reaction of dimeric P1 units converting to isolated P0 units and P2 chains associated with oxygen incorporation. These results highlight that universal MLIPs are valuable starting points for approximate simulations or database generation, but fine-tuning on both composition and relevant structural features is essential to accurately reproduce the short- and medium-range order of NaPSO glasses.