标题： 通用机器学习势函数在生物分子模拟中显式长程相互作用的性能 显示英文标题

标题： Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations

摘要： 通用机器学习势能在化学组成和振动自由度上展现出可转移的准确性，但其在生物分子模拟中的应用仍鲜有探索。 本工作系统评估了在SPICE-v2数据集上训练的等变消息传递架构，包括显式长程色散和静电相互作用的情况。 我们评估了模型大小、训练数据组成以及静电处理方式对分布内和分布外基准数据集的影响，以及对体相液态水、水溶液氯化钠和生物分子（包括丙氨酸三肽、小蛋白Trp-cage和Crambin）的分子模拟结果的影响。 虽然更大的模型在基准数据集上提高了准确性，但这种趋势并不总能扩展到模拟获得的性质。 预测性质也依赖于训练数据集的组成。 长程静电相互作用在不同系统中没有系统性影响。 然而，对于Trp-cage，它们的引入会增加构象多样性。 我们的结果表明，不平衡的数据集和不成熟的评估方法目前阻碍了通用机器学习势能在生物分子模拟中的应用。 显示英文摘要

摘要： Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remains underexplored. This work systematically evaluates equivariant message-passing architectures trained on the SPICE-v2 dataset with and without explicit long-range dispersion and electrostatics. We assess the impact of model size, training data composition, and electrostatic treatment across in- and out-of-distribution benchmark datasets, as well as molecular simulations of bulk liquid water, aqueous NaCl solutions, and biomolecules, including alanine tripeptide, the mini-protein Trp-cage, and Crambin. While larger models improve accuracy on benchmark datasets, this trend does not consistently extend to properties obtained from simulations. Predicted properties also depend on the composition of the training dataset. Long-range electrostatics show no systematic impact across systems. However, for Trp-cage, their inclusion yields increased conformational variability. Our results suggest that imbalanced datasets and immature evaluation practices currently challenge the applicability of universal machine-learned potentials to biomolecular simulations.