用深度平衡模型提升分子动力学模拟速度与精度
DEQuify your force field: More efficient simulations using deep equilibrium models
- 将等变神经网络改造成深度平衡模型，复用前步中间特征
- 在多个数据集上实现10%-20%的加速与精度提升
- 训练内存更省，适合更大系统和更复杂模型
机器学习力场在分子动力学模拟中展现出比人工构建力场更高的准确性。近年来的进步主要得益于对物理系统的先验知识利用，特别是旋转、平移和反射对称性。本文指出，另一个尚未被充分挖掘的重要先验信息是：分子系统模拟具有连续性，相邻状态极为相似。为此，我们提出将先进的等变基础模型重构为深度平衡模型（DEQ），从而复用前一时间步的神经网络中间特征。该方法在MD17、MD22和OC20 200k数据集上，相较非DEQ基线模型实现了10%-20%的速度与精度提升。同时训练过程内存消耗显著降低，使更大规模系统和更复杂模型的训练成为可能。
原文摘要 · Abstract (English)
Machine learning force fields show great promise in enabling more accurate molecular dynamics simulations compared to manually derived ones. Much of the progress in recent years was driven by exploiting prior knowledge about physical systems, in particular symmetries under rotation, translation, and reflections. In this paper, we argue that there is another important piece of prior information that, thus fa,r hasn't been explored: Simulating a molecular system is necessarily continuous, and successive states are therefore extremely similar. Our contribution is to show that we can exploit this information by recasting a state-of-the-art equivariant base model as a deep equilibrium model. This allows us to recycle intermediate neural network features from previous time steps, enabling us to improve both accuracy and speed by $10\%-20\%$ on the MD17, MD22, and OC20 200k datasets, compared to the non-DEQ base model. The training is also much more memory efficient, allowing us to train more expressive models on larger systems.
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