Optimizing Machine Learning Potentials for Hydroxide Transport: Surprising Efficiency of Single-Concentration Training

Abstract: We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solutions as a case study. Despite containing identical chemical species (K$^+$, OH$^-$, H$_2$O) across all concentrations, models fine-tuned on specific KOH concentrations exhibit surprisingly poor transferability to others, with force prediction errors increasing dramatically from ~30 meV/A (at training concentration) to ~90 meV/A (at very different concentrations). This reveals a critical limitation when applying such models beyond their training domain, even within chemically homogeneous systems. We demonstrate that strategic selection of training data can substantially overcome these limitations without requiring extensive computational resources. Models fine-tuned on intermediate concentrations (6.26 mol/L) exhibit remarkable transferability across the entire concentration spectrum (0.56-17.98 mol/L), often outperforming more computationally expensive models trained on multiple concentration datasets. This approach enables accurate simulation of hydroxide transport dynamics across varying electrolyte conditions while maintaining near-quantum accuracy. Our simulations further reveal the emergence of hydroxide-hydroxide hydrogen bonding at high concentrations - a phenomenon not explicitly represented in dilute training data but successfully captured by our intermediate-concentration model. This work establishes practical guidelines for developing broadly applicable machine learning force fields with optimal transferability, challenging the assumption that diverse training datasets are always necessary for robust performance in similar chemical environments.