将扩散模型与分子动力学结合，无需力数据即可训练高精度原子势能模型。
On the Connection Between Diffusion Models and Molecular Dynamics
- 通过新推导揭示噪声与原子受力的数学关联
- 在粗粒化氯化锂溶液上实现稳定模拟，性能随数据增强提升
- 可直接对接主流分子动力学软件，便于实际应用
神经网络势函数（NNPs）已成为高效高精度建模原子相互作用的强大工具。最近，去噪扩散模型通过训练网络去除稳定构型中的噪声，在无需力数据的情况下展现潜力。本文通过新的简化数学推导，阐明了噪声与力之间的关系，并展示如何利用标准NNP架构与常规分子动力学软件包结合实现去噪模型。我们在粗粒化氯化锂溶液上训练了一个基于扩散的NNP，并通过数据复制策略提升模型性能。
原文摘要 · Abstract (English)
Neural Network Potentials (NNPs) have emerged as a powerful tool for modelling atomic interactions with high accuracy and computational efficiency. Recently, denoising diffusion models have shown promise in NNPs by training networks to remove noise added to stable configurations, eliminating the need for force data during training. In this work, we explore the connection between noise and forces by providing a new, simplified mathematical derivation of their relationship. We also demonstrate how a denoising model can be implemented using a conventional MD software package interfaced with a standard NNP architecture. We demonstrate the approach by training a diffusion-based NNP to simulate a coarse-grained lithium chloride solution and employ data duplication to enhance model performance.
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