EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
EquiFiLM introduces a lightweight, backbone-agnostic extension to equivariant foundation MLFFs by adding continuous external conditioning via per-layer Feature-wise Linear Modulation (FiLM) blocks. The method preserves exact E(3)-equivariance by modulating only scalar channels, enabling the modeling of non-equilibrium states like charging, applied fields, or electronic excitation. Demonstrated on charged liquid water using MACE-MatPES as the backbone, E-MACE achieves significant error reductions
Analysis
TL;DR
- EquiFiLM introduces a lightweight, backbone-agnostic extension to equivariant foundation MLFFs by adding continuous external conditioning via per-layer Feature-wise Linear Modulation (FiLM) blocks.
- The method preserves exact E(3)-equivariance by modulating only scalar channels, enabling the modeling of non-equilibrium states like charging, applied fields, or electronic excitation.
- Demonstrated on charged liquid water using MACE-MatPES as the backbone, E-MACE achieves significant error reductions compared to baselines without the extension.
- Training requires only a few thousand DFT-labeled frames, offering a massive data efficiency advantage over training charge-aware foundations from scratch which need approximately 10^8 structures.
- The model supports stable molecular dynamics across a wide range of conditions and accurately predicts charge-dependent structural responses such as the reduced pair distribution function.
Why It Matters
This research addresses a critical limitation in current foundation machine learning force fields, which are typically restricted to equilibrium ground-state physics. By enabling native handling of externally induced electronic state changes, EquiFiLM expands the applicability of high-accuracy MLFFs to driven processes like photoexcitation and charge injection, which are central to materials science and chemical physics.
Technical Details
- Architecture: Utilizes a per-layer FiLM block to inject continuous external conditioning into any equivariant foundation MLFF. The modulation is applied exclusively to scalar channels to maintain strict E(3)-equivariance.
- Performance Metrics: On four training charges, E-MACE reduces force RMSE by 3.1x (from 21.3 to 6.96 meV/Angstrom) and per-atom energy RMSE by 61x (from 6.1 to 0.1 meV/atom) compared to a baseline without EquiFiLM.
- Generalization: Across seven held-out interpolation and extrapolation charges, force RMSE remains within 18-61 meV/Angstrom and energy RMSE within 0.7-5.4 meV/atom, demonstrating robust generalization capabilities.
- Data Efficiency: Requires only a few thousand DFT-labeled frames for fine-tuning, contrasting sharply with the ~10^8 structures needed for training a charge-aware foundation model from scratch.
- Validation: The model runs stable molecular dynamics and successfully predicts the charge-dependent first-shell response of the reduced pair distribution function, validated against ultrafast electron diffraction probes.
Industry Insight
- Accelerated Discovery: Researchers can rapidly adapt existing foundation models for specific non-equilibrium scenarios without the prohibitive cost of collecting massive new datasets, significantly speeding up simulations of complex driven processes.
- Standardization of Conditioning: The backbone-agnostic nature of the recipe suggests that FiLM-based conditioning could become a standard module for extending equivariant models to handle diverse external parameters beyond just charge, such as electric fields or temperature gradients.
- Cost-Effective Simulation: The indistinguishable inference cost compared to base models makes this approach highly attractive for industrial applications requiring long-timescale MD simulations under varying external conditions, such as battery operation or photovoltaic material behavior.
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