Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction

2026-06-08

AI Summary

Molecular force prediction is often hindered by a mismatch between predefined spatial scales and the optimal modeling scale for a given task. This work proposes a loss-guided adaptive scale optimization framework that, for the first time, leverages loss signals to drive multi-scale selection in molecular representation learning, thereby transcending conventional fixed-scale modeling paradigms. The approach integrates hard routing, continuous interpolation, differentiable scale updating, and a dynamic scale-pool refinement mechanism to automatically search for an improved modeling resolution starting from an initial anchor scale. Evaluated on an aqueous NaCl system, the method reduces the overall force prediction mean absolute error (MAE) to 381.23 meV/Å, with a marked improvement in the near-contact region (<0.6 nm), where the MAE drops to 260.51 meV/Å.

Abstract

Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic and solvent-mediated effects. However, most molecular representation learning methods rely on manually predefined scales, and the task-optimal modeling scale may not coincide with these fixed levels. This study introduces a loss-guided adaptive scale refinement framework for molecular force prediction, treating predefined scales as initial anchors and discovering task-effective resolutions through interpolation, routing, differentiable scale updates, and scale pool refinement. Using a NaCl aqueous ionic system as a minimal testbed, this study constructs short-scale and long-range force prediction branches and analyzes their complementarity. Oracle hard routing reduces the overall force MAE from 399.65 to 382.67, while continuous oracle interpolation further reduces it to 380.96. In close-contact regimes with nearest-ion distance below 0.6 nm, the close-contact MAE decreases from 327.22 to 260.51. A minimal scale pool update experiment shows that starting from endpoint anchors {0,1}, loss-guided updates automatically generate intermediate scales and recover most of the continuous oracle performance. The final updated scale pool {0,0.125,0.25,0.375,0.5,0.75,1} achieves an overall MAE of 381.23. These results support adaptive scale refinement as a promising direction for molecular representation learning, especially when fixed-scale modeling is insufficient.

Problem

• molecular force prediction
• adaptive scale refinement
• multi-scale modeling
• representation learning
• spatial scales

Innovation

• adaptive scale refinement
• molecular force prediction
• loss-guided optimization
• multi-scale representation
• differentiable scale update