让力场模型能随电荷变化自动调整，仅用少量数据即可实现高精度模拟。
EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
- 通过特征调制块动态调节力场，支持连续电荷条件输入。
- 电荷变化下力误差降低3.1倍，能量误差降低61倍，保持高效推理。
- 适用于光激发、电荷注入等非平衡过程，适合材料设计与超快实验模拟。
如MACE-MP-0和UMA等基础机器学习力场（MLFF）在接近密度泛函理论（DFT）精度下覆盖广泛化学空间，但假设系统处于平衡基态，无法原生处理外部电荷、电场或电子激发等引起的电子态变化，限制了其在光激发、电荷注入等驱动过程中的应用。本文提出EquiFiLM，一种轻量级扩展方法，通过逐层特征式线性调制（FiLM）块为任意等变基础MLFF添加连续外部条件输入，仅需少量训练数据即可学习外部驱动下的势能面变化。该模块仅调制标量通道，精确保持E(3)等变性。以MACE-MatPES为基础模型，在带电液态水中构建E-MACE模型。在四个训练电荷上，相比未使用EquiFiLM的基线模型，力均方根误差从21.3降至6.96 meV/Å（降低3.1倍），原子能量误差从6.1降至0.1 meV/atom（降低61倍），推理成本不变。在七个未见电荷的插值与外推测试中，力误差保持在18–61 meV/Å，能量误差在0.7–5.4 meV/atom。模型在全范围电荷下稳定运行分子动力学，并准确预测超快电子衍射探测的电荷依赖第一壳层响应。将此条件引入基础模型仅需数千个DFT标注帧，远低于约10⁸结构的电荷感知模型从头训练。该方法对骨干模型和条件类型均无依赖，适用于任何具有标量交互通道的等变MLFF。
原文摘要 · Abstract (English)
Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilibrium ground-state physics and do not natively handle externally induced changes to the electronic state, such as charging, applied fields, or electronic excitation, which limits their use for driven processes such as photoexcitation and charge injection. We propose EquiFiLM, a lightweight extension that adds continuous external conditioning to any equivariant foundation MLFF via a per-layer Feature-wise Linear Modulation (FiLM) block, learning externally driven changes to the potential energy surface from minimal training data. The block modulates only scalar channels and preserves E(3)-equivariance exactly. We demonstrate the recipe on charged liquid water with the foundation model MACE-MatPES as the backbone, yielding E-MACE. On the four training charges, E-MACE delivers a $3.1\times$ reduction in force RMSE ($21.3$ to $6.96$ meV/$\mathring{A}$) and a $61\times$ reduction in per-atom energy RMSE ($6.1$ to $0.1$ meV/atom) over a baseline without EquiFiLM trained on the same data, at indistinguishable inference cost. Across seven held-out interpolation and extrapolation charges, force RMSE stays within $18-61$ meV/$\mathring{A}$ and energy RMSE within $0.7-5.4$ meV/atom. The model runs stable molecular dynamics across the full range tested and predicts the charge-dependent first-shell response of the reduced pair distribution function probed by ultrafast electron diffraction. Adding this conditioning axis to the foundation requires only a few thousand DFT-labeled frames, against the $\approx 10^8$ structures of a charge-aware foundation trained from scratch. The recipe is backbone- and conditioning-agnostic: it applies without architectural change to any equivariant MLFF with scalar interaction-layer channels.
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