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Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling. 

Abstract The development of all‐solid‐state Li‐ion batteries requires solid electrolyte materials with many desired properties, such as ionic conductivity, chemical and electrochemical stability, and mechanical durability. Computation‐guided materials design techniques are advantageous in designing and identifying new solid electrolytes that can simultaneously meet these requirements. In this joint computational and experimental study, a new family of fast lithium ion conductors, namely, LiTaSiO₅with sphene structure, are successfully identified, synthesized, and demonstrated using a novel computational design strategy. First‐principles computation predicts that Zr‐doped LiTaSiO₅sphene materials have fast Li diffusion, good phase stability, and poor electronic conductivity, which are ideal for solid electrolytes. Experiments confirm that Zr‐doped LiTaSiO₅sphene structure indeed exhibits encouraging ionic conductivity. The lithium diffusion mechanisms in this material are also investigated, indicating the sphene materials are 3D conductors with facile 1D diffusion along the [101] direction and additional cross‐channel migration. This study demonstrates a novel design strategy of activating fast Li ionic diffusion in lithium sphenes, a new materials family of superionic conductors.