标题： MOFSimBench：评估金属-有机框架分子建模中的通用机器学习原子间势能函数 显示英文标题

标题： MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling

摘要： 通用机器学习原子间势（uMLIPs）已成为加速原子模拟的强大工具，提供可扩展且高效的建模，其准确性接近量子计算。然而，它们在实际、现实应用中的可靠性和有效性仍然是一个开放问题。金属有机框架（MOFs）及相关纳米多孔材料是高度多孔的晶体，在碳捕获、能源存储和催化应用中具有关键相关性。由于其多样的化学性质、结构复杂性（包括孔隙和配位键），以及现有训练数据集中不存在，建模纳米多孔材料对uMLIPs提出了独特的挑战。在此，我们引入MOFSimBench，这是一个基准测试，用于评估uMLIPs在纳米多孔材料的关键材料建模任务上的表现，包括结构优化、分子动力学（MD）稳定性、体模量和比热等体性质的预测以及客体-主体相互作用。在化学和结构多样化的材料集上评估了20多个不同架构的模型，我们发现表现最好的uMLIPs在所有任务中始终优于经典力场和微调的机器学习势能，证明了它们在纳米多孔材料建模中的部署准备就绪。我们的分析表明，数据质量，特别是训练集的多样性以及非平衡构象的包含，在决定所有评估的uMLIPs性能方面比模型架构更为关键。我们已在https://github.com/AI4ChemS/mofsim-bench发布我们的模块化和可扩展的基准测试框架，提供一个开放资源，以指导纳米多孔材料建模的采用和uMLIPs的进一步发展。 显示英文摘要

摘要： Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliability and effectiveness in practical, real-world applications remain an open question. Metal-organic frameworks (MOFs) and related nanoporous materials are highly porous crystals with critical relevance in carbon capture, energy storage, and catalysis applications. Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry, structural complexity, including porosity and coordination bonds, and the absence from existing training datasets. Here, we introduce MOFSimBench, a benchmark to evaluate uMLIPs on key materials modeling tasks for nanoporous materials, including structural optimization, molecular dynamics (MD) stability, the prediction of bulk properties, such as bulk modulus and heat capacity, and guest-host interactions. Evaluating over 20 models from various architectures on a chemically and structurally diverse materials set, we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks, demonstrating their readiness for deployment in nanoporous materials modeling. Our analysis highlights that data quality, particularly the diversity of training sets and inclusion of out-of-equilibrium conformations, plays a more critical role than model architecture in determining performance across all evaluated uMLIPs. We release our modular and extendable benchmarking framework at https://github.com/AI4ChemS/mofsim-bench, providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs.