DBMLFF: Linear scaling machine learning force fields via electron density decomposition for molecular electrolytes

Abstract

Machine learning force fields (MLFF) are rapidly evolving to provide molecular dynamics simulations of molecules and materials with an accuracy comparable to ab initio methods, while significantly reducing the need for computational resources. However, conventional MLFFs are generally system-specific; introducing new chemical components requires assembling a new training dataset and retraining the entire model from scratch. To address this, we present a density-based machine learning force field (DBMLFF). The key advantage of DBMLFF lies in its modular parametrization strategy: by modeling each molecular species independently, the resulting force fields achieve seamless transferability across diverse chemical environments and retaining high accuracy without the need for retraining. This significantly improves model portability and cross-system applicability. Unlike most of the statistically based MLFFs, DBMLFF is a physics-based force field with machine learning components in it. It computes intermolecular interactions directly from electron density, enabling accurate descriptions of complex non-bonded behavior. In terms of computational efficiency, DBMLFF is three orders of magnitude faster than ab initio molecular dynamics and exhibits linear scaling with system size, allowing efficient simulations of large-scale systems. These features make DBMLFF a robust tool for multi-component electrolyte MD simulations, ideal for practical electrochemical systems with variable compositions and large scales.