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This study evaluates the performance of two pretrained machine-learning interatomic potentials, MACE-MP-0 and MACE-POLAR-1, in simulating ionic-liquid impacts on extractor surfaces in electrospray thrusters, benchmarking them against density-functional-theory molecular dynamics (DFT/MD) and reactive force fields (ReaxFF). The models successfully replicate key collision outcomes, such as ionic dissociation and HF formation, while demonstrating significant computational efficiency, completing simulations in minutes compared to hours for DFT/MD. These findings highlight the potential of machine-learning models to provide a cost-effective alternative for high-fidelity simulations in electrospray-thruster applications.
Machine-learning potentials can achieve DFT-level accuracy in simulating ionic-liquid impacts while being four orders of magnitude faster than traditional methods.
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.
