Abstract
Machine learning interatomic potentials (MLIPs) have emerged as efficient surrogates for quantum mechanical calculations, offering substantial acceleration in ab-initio atomistic simulations. While most applications of MLIPs have focused on isolated molecules, extending their accuracy and transferability to predict condensed-phase properties of liquids remains a major challenge. In this work, we present a novel framework for constructing transferable and data-efficient force fields for organic liquids using a dual-space active learning (AL) strategy. This approach enables an efficient AL workflow across both configurational and chemical space by coupling a query-by-committee method with an explicitly constructed target chemical space, generated using computationally inexpensive classical methods. Our approach employs a Euclidean transformer architecture to train the Neural Network Potential for Liquid Simulations (NPLS). As a proof of concept, we target the complete alkane family and train NPLS using high-level DFT data. The resulting model is rigorously benchmarked against experimental measurements and the widely used classical force field, i.e., OPLS-AA, across thermodynamic, dynamic, and phase transition properties. Remarkably, the NPLS model demonstrates strong generalization to larger systems of polyolefins and accurately captures liquid-to-solid transitions. However, systematic deviations are observed, particularly in the predicted liquid densities. We attribute these discrepancies to the non-negligible influence of nuclear quantum fluctuations (NQF), which are not captured with classical molecular dynamics (MD) sampling. To address this, we perform path-integral (PI) MD simulations to incorporate NQF, predictions of which show significantly improved agreement with experimental measurements. The NLPS-PIMD results outperform the OPLS-AA force fields in accuracy. We believe this methodology provides a practical and extensible route for developing highly accurate and transferable bespoke or universal MLIPs for complex liquids.