AI 中文总结
研究对六个通用机器学习力场模型用于月球矿物分子动力学模拟进行基准测试，通过多种方式评估结构保真度，测试羟基化表面，还进行性能基准测试，为模型应用提供初步基准并明确未来关键方向。
AI 中文摘要
通用机器学习原子间势为加速材料分子动力学模拟提供了一条有前景的途径，但它们对与月球风化层相关的硅酸盐、氧化物和含氢表面物种的可转移性仍有待阐明。本文使用四个代表性月球矿物（镁橄榄石、铁橄榄石、钛铁矿和钙长石）的NVT分子动力学模拟，对六个基础模型（MACE-MH、MatterSim、SevenNet-0、UPET、UMA和NequIP-OAM-L）进行基准测试。通过温度稳定性、键距统计、键角分布和部分径向分布函数评估结构保真度，并与晶体学参考数据进行比较。模型对Si--O、Mg--O、Al--O和Ca--O局部环境的再现较好，而Fe--O和Ti--O配位环境的分布更宽且短时间尺度波动更大。羟基化表面测试表明各模型和矿物的O--H键距分布一致。在单个NVIDIA RTX 4090上的性能基准测试表明，SevenNet-0、MatterSim和UPET在六个测试模型中吞吐量最高，MACE-MH以中等成本仍具实用性，UMA和NequIP-OAM-L在更高运行时成本和内存需求下扩展了对更新基础势的比较。这些结果为将通用基础模型应用于月球矿物模拟提供了初步基准，并确定了未来从头验证、模型微调以及应用于月球挥发物演化、空间风化、原位资源利用和极地样本返回研究的关键方向。
英文摘要
Universal machine-learning interatomic potentials provide a promising route for accelerating molecular dynamics simulations of materials, but their transferability to lunar regolith-relevant silicates, oxides, and hydrogen-bearing surface species remains elucidated. Here, we benchmark six foundation models, MACE-MH, MatterSim, SevenNet-0, UPET, UMA, and NequIP-OAM-L, using NVT molecular dynamics simulations of four representative lunar minerals: forsterite, fayalite, ilmenite, and anorthite. Structural fidelity is evaluated using temperature stability, bond-distance statistics, bond-angle distributions, and partial radial distribution functions, with comparison to crystallographic reference data. The models reproduce Si--O, Mg--O, Al--O, and Ca--O local environments reasonably well, while Fe--O and Ti--O coordination environments show broader distributions and larger short-timescale fluctuations, highlighting the need for further validation and fine tuning with additional ground truth data for Fe- and Ti-bearing lunar phases. Hydroxylated surface tests show consistent O--H bond-distance distributions across models and minerals, suggesting that these foundation models may provide useful starting points for screening surface hydroxyl stability and volatile-related processes. Performance benchmarks on a single NVIDIA RTX 4090 show that SevenNet-0, MatterSim, and UPET provide the highest throughput among the six tested models, MACE-MH remains practical at intermediate cost, and UMA and NequIP-OAM-L extend the comparison to newer foundation potentials at higher runtime cost and memory demand. These results provide an initial benchmark for applying universal foundation models to lunar mineral simulations and identify key directions for future ab initio validation, model fine-tuning, and applications to lunar volatile evolution, space weathering, ISRU, and polar sample return studies.
Comments14 pages, 7 figures, 2 tables
