Abstract
<jats:p>Machine-learned interatomic potentials (ML-IAPs) have emerged as a powerful tool for achieving nominally quantum-accurate simulations at reduced computational cost. However, for covalently bonded systems, a fundamental tension exists between the model size required to resolve highly featured short-range interactions and the extended interaction range needed to capture smoother long-range contributions. This work introduces a multi-layer representation that resolves this tension by decomposing the potential energy surface into two overlaid models: a short-range layer employing a dense basis to capture bond rearrangements and repulsion, and a long-range layer employing a sparser basis for smoothly varying contributions. This strategy is implemented within the ChIMES ML-IAP framework and demonstrated on three systems of increasing complexity: a classical united-atom propane model, water across non-reactive and reactive thermodynamic conditions, and reactive C/O mixtures spanning a broad range of temperatures, pressures, and compositions. In each case, multi-layer models achieve accuracy comparable to single-layer models while yielding at least an order-of-magnitude reduction in computational cost. A comprehensive hyperparameter sensitivity study on the propane system provides physically motivated heuristics for selecting additional hyperparameter introduced through the multi-layer strategy. Taken together, these results establish multi-layer ChIMES as a general and practical strategy for improving the efficiency of bespoke ML-IAPs without sacrificing predictive accuracy.</jats:p>