Title: Generative Latent Space Dynamics of Electron Density Show Chinese title

Title: 电子密度的生成潜在空间动力学

Abstract: Modeling the time-dependent evolution of electron density is essential for understanding quantum mechanical behaviors of condensed matter and enabling predictive simulations in spectroscopy, photochemistry, and ultrafast science. Yet, while machine learning methods have advanced static density prediction, modeling its spatiotemporal dynamics remains largely unexplored. In this work, we introduce a generative framework that combines a 3D convolutional autoencoder with a latent diffusion model (LDM) to learn electron density trajectories from ab-initio molecular dynamics (AIMD) simulations. Our method encodes electron densities into a compact latent space and predicts their future states by sampling from the learned conditional distribution, enabling stable long-horizon rollouts without drift or collapse. To preserve statistical fidelity, we incorporate a scaled Jensen-Shannon divergence regularization that aligns generated and reference density distributions. On AIMD trajectories of liquid lithium at 800 K, our model accurately captures both the spatial correlations and the log-normal-like statistical structure of the density. The proposed framework has the potential to accelerate the simulation of quantum dynamics and overcome key challenges faced by current spatiotemporal machine learning methods as surrogates of quantum mechanical simulators. Show Chinese abstract

Abstract: 模拟电子密度随时间演变的过程对于理解凝聚态物质的量子力学行为以及在光谱学、光化学和超快科学中实现预测性模拟至关重要。然而，尽管机器学习方法在静态密度预测方面取得了进展，但对其时空动态建模的研究仍 largely 未被探索。在本工作中，我们引入了一种生成框架，该框架结合了三维卷积自编码器和潜在扩散模型（LDM），以从第一性原理分子动力学（AIMD）模拟中学习电子密度轨迹。我们的方法将电子密度编码到一个紧凑的潜在空间中，并通过从学习到的条件分布中采样来预测其未来状态，从而在没有漂移或崩溃的情况下实现稳定的长时程滚动。为了保持统计保真度，我们引入了一个缩放的詹森-香农散度正则化项，以对齐生成的和参考的密度分布。在800 K下液态锂的AIMD轨迹上，我们的模型准确捕捉了密度的空间相关性和类似对数正态的统计结构。所提出的框架有望加速量子动力学的模拟，并克服当前时空机器学习方法作为量子力学模拟替代品所面临的关键挑战。