We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell-Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7-0.8 for lithium mean squared displacement (MSD) predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations while maintaining high accuracy. With speed-ups of up to 600,000$\times$ compared to first-principles methods, LiFlow enables scalable simulations at significantly larger length and time scales.

翻译：我们提出了LiFlow，一种用于加速晶体材料分子动力学（MD）模拟的生成式框架，该框架将任务表述为原子位移的条件生成。该模型采用流匹配方法，其中包含一个用于生成原子位移的传播子子模型和一个用于局部修正非物理几何结构的校正器，并整合了一个基于麦克斯韦-玻尔兹曼分布的自适应先验，以考虑化学和热条件。我们在一个包含4,186种固态电解质（SSE）候选材料在四个温度下25皮秒轨迹的数据集上对LiFlow进行了基准测试。该模型在未见过的成分上对锂离子均方位移（MSD）的预测获得了0.7-0.8的一致斯皮尔曼等级相关性。此外，LiFlow能够从短训练轨迹泛化到更大的超晶胞和更长的模拟，同时保持高精度。与第一性原理方法相比，LiFlow实现了高达600,000倍的加速，从而能够在显著更大的长度和时间尺度上进行可扩展的模拟。