AI 中文总结
该研究针对电喷推进器离子液体撞击模拟的成本与保真度矛盾，测试MACE系列预训练机器学习势，发现其兼顾类DFT保真度与低成本，为推进器模拟提供了实用中间方案。
AI 中文摘要
预测离子液体对提取器表面的撞击产物对电喷推进器的寿命分析至关重要，但现有的原子级方法需要在化学保真度和计算成本之间做出权衡。反应力场可实现高通量采样，但无法明确解析电子电荷的重新分布，且可能会遗漏撞击过程中相关的反应路径；而混合量子-经典密度泛函理论分子动力学（DFT/MD）虽能捕捉电荷重新分布和中性产物的形成，但其计算成本要高得多。预训练的原子级基础模型近来成为以低得多的成本实现类DFT化学保真度的潜在途径。在此，我们针对1-乙基-3-甲基咪唑四氟硼酸盐（EMI-BF₄）的几何优化，以及其以10-100 eV的能量撞击金提取器模型表面的过程，对两种预训练的机器学习原子间势MACE-MP-0（中等规模）和MACE-POLAR-1，与DFT/MD及ReaxFF进行了基准测试。这些模型重现了DFT/MD中观测到的多种碰撞结果，包括离子解离、高能共价碎片化，尤其是通过类中和化学过程形成的HF，这是ReaxFF模拟中未捕捉到的。在计算性能基准测试中，MACE-POLAR-1和MACE-MP-0（中等规模）分别在5.12分钟和2.54分钟内完成了每条2皮秒的轨迹，在报告的基准测试条件下，其 wall time 比DFT/MD参考值短约四个数量级。这些结果表明，预训练的机器学习势是一种实用的中等成本方法，可用于化学分辨率的电喷撞击模拟，并为利用DFT数据进行针对性微调以拓展其在电喷推进器和电推进建模中的更广泛应用提供了动力。
英文摘要
Predicting the products of ionic-liquid impacts on extractor surfaces is important for electrospray-thruster lifetime analysis, yet available atomistic methods require a compromise between chemical fidelity and computational cost. Reactive force fields enable high-throughput sampling but do not explicitly resolve electronic charge redistribution and may miss relevant reaction pathways during impact, whereas mixed quantum--classical density-functional-theory molecular dynamics (DFT/MD) can capture charge redistribution and neutral-product formation at substantially higher computational cost. Pretrained atomistic foundation models have recently emerged as a potential route toward DFT-like chemical fidelity at considerably lower cost. Here, we benchmark two pretrained machine-learning interatomic potentials, MACE-MP-0 (medium) and MACE-POLAR-1, against DFT/MD and ReaxFF for geometry optimization of 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF$_4$) and for 10-100 eV impacts on a model Au extractor surface. The models reproduce several collision outcomes observed in DFT/MD, including ionic dissociation, high-energy covalent fragmentation, and, in particular, HF formation through neutralization-like chemistry that is not captured in the ReaxFF simulations. In the computational-performance benchmark, MACE-POLAR-1 and MACE-MP-0 (medium) completed each 2~ps trajectory in 5.12 and 2.54~min, respectively, corresponding to wall times approximately four orders of magnitude shorter than the DFT/MD reference under the reported benchmark conditions. These results support pretrained machine-learning potentials as a practical intermediate-cost approach for chemically resolved electrospray-impact simulations and motivate targeted fine-tuning with DFT data for broader applications in electrospray-thruster and electric-propulsion modeling.
Comments19 pages, 6 figures
