用扩散模型预测分子自组装结构，加速新材料发现。
MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly
- 基于分子动力学数据训练扩散模型，输入力场即可生成结构。
- 生成结构准确率显著高于传统点云扩散模型。
- 适合材料设计、分子模拟领域研究者快速探索新结构。
新材料的发现与研究依赖于分子模拟，但计算成本高昂。本文提出MDDM——一种分子动力学扩散模型，可针对任意输入势函数预测有效构型。在大规模分子动力学自组装数据上训练后，该模型能将均匀噪声转化为对应于输入势的合理粒子结构。其架构内置周期边界条件满足性与平移不变性等领域特性。在无条件与条件生成任务中，该模型均显著优于基线点云扩散模型。
原文摘要 · Abstract (English)
The discovery and study of new material systems rely on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusion Model, which is capable of predicting a valid output conformation for a given input pair potential function. After training MDDM on a large dataset of molecular dynamics self-assembly results, the proposed model can convert uniform noise into a meaningful output particle structure corresponding to an arbitrary input potential. The model's architecture has domain-specific properties built-in, such as satisfying periodic boundaries and being invariant to translation. The model significantly outperforms the baseline point-cloud diffusion model for both unconditional and conditional generation tasks.
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