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Abstract Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties. 

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 Understanding the electrochemical deposition of metal anodes is critical for high-energy rechargeable batteries, among which solid-state lithium metal batteries have attracted extensive interest. A long-standing open question is how electrochemically deposited lithium-ions at the interfaces with the solid-electrolytes crystalize into lithium metal. Here, using large-scale molecular dynamics simulations, we study and reveal the atomistic pathways and energy barriers of lithium crystallization at the solid interfaces. In contrast to the conventional understanding, lithium crystallization takes multi-step pathways mediated by interfacial lithium atoms with disordered and random-closed-packed configurations as intermediate steps, which give rise to the energy barrier of crystallization. This understanding of multi-step crystallization pathways extends the applicability of Ostwald’s step rule to interfacial atom states, and enables a rational strategy for lower-barrier crystallization by promoting favorable interfacial atom states as intermediate steps through interfacial engineering. Our findings open rationally guided avenues of interfacial engineering for facilitating the crystallization in metal electrodes for solid-state batteries and can be generally applicable for fast crystal growth.