EquiFiLM Enhances ML Force Fields with Charge Conditioning

Foundation machine learning force fields (MLFFs) like MACE-MP-0 and UMA are highly accurate for broad chemical spaces but typically assume equilibrium ground-state physics. This limits their application to dynamic processes involving external influences like charging, applied fields, or electronic excitation. EquiFiLM is introduced as a lightweight, backbone-agnostic extension that integrates continuous external conditioning into any equivariant foundation MLFF. It uses a per-layer Feature-wise Linear Modulation (FiLM) block to learn how external factors modify the potential energy surface, requiring only minimal training data. The method preserves E(3)-equivariance and, when applied to charged liquid water with MACE-MatPES, significantly reduces force and energy RMSE compared to baselines. This allows MLFFs to accurately predict charge-dependent molecular dynamics and responses with far less data than training a charge-aware model from scratch.