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.

机器学习原子间势（MLIP）已广泛应用于原子模拟。尽管已有关于 MLIP 的错误和差异的报告，但仍缺乏对 MLIP 在广泛的材料性能上的性能的全面检查。本研究介绍了一个分析过程，包括模型采样、基准测试、误差评估和对 MLIP 集合的多维统计分析，以预测各种属性的误差。通过对基于六种不同 MLIP 类型的 2300 个 MLIP 模型进行分析，确定了对 MLIP 实现小误差构成挑战的几个属性。对两个或多个属性的帕累托前沿分析揭示了 MLIP 不同属性的权衡，强调了同时实现大量属性的低误差的困难。此外，我们提出相关图分析来表征 MLIP 的误差性能，并选择代表属性来预测其他属性误差。对 MLIP 模型大型数据集的分析过程揭示了 MLIP 性能的潜在复杂性，为 MLIP 的未来开发提供了重要指导，提高了一系列材料特性的预测准确性。