Why MLFF Robustness Matters

The opportunity

Density functional theory provides a physically grounded route to calculating energies, forces, and other atomic properties, but its computational cost limits the length and scale of many simulations. MLFFs learn from reference electronic-structure calculations and can evaluate atomic configurations much more quickly.

This speed makes MLFFs attractive for relaxation, molecular dynamics, materials screening, and deformation studies.

The risk

An MLFF is most trustworthy near configurations represented by its training distribution. During a simulation, atoms may enter unfamiliar arrangements because of thermal motion, strain, defects, interfaces, optimization steps, or numerical instability.

A small force error can alter the next atomic update. That update changes the next prediction, allowing an initially small error to redirect an entire relaxation trajectory.

Research question

This work tests whether small, deliberately challenging coordinate perturbations expose model-specific failure modes.

The central question is not simply whether a model predicts a large force. It is whether the model can guide the perturbed structure back toward the same physically meaningful configuration reached before the attack.

Why this helps other researchers

A reproducible robustness benchmark can help researchers:

• identify perturbation magnitudes at which predictions become unstable;

• compare architectures and training distributions under matched conditions;

• distinguish recoverable perturbations from persistent structural changes;

• detect catastrophic outliers hidden by average-error metrics;

• select models and numerical precision appropriate for a simulation;

• establish validation checks before deploying an MLFF in a new domain.

The benchmark therefore complements conventional test-set accuracy. It asks how prediction errors behave inside an iterative physical workflow, where each prediction influences the next configuration.