标题： BoostMD：通过利用之前时间步的机器学习力场特征加速分子采样 显示英文标题

标题： BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps

摘要： 模拟原子尺度过程，如蛋白质动力学和催化反应，对于生物学、化学和材料科学的进步至关重要。 机器学习力场（MLFFs）已成为强大的工具，能够实现接近量子力学的准确性，并具有良好的泛化能力。 然而，与经典力场相比，它们在推理时间上通常较长，特别是在运行许多生物应用所需的大量分子动力学（MD）模拟时。 在本研究中，我们引入了BoostMD，一种旨在加速MD模拟的代理模型架构。 BoostMD利用之前时间步计算的节点特征，根据位置变化预测能量和力。 这种方法降低了学习任务的复杂性，使BoostMD比传统的MLFFs更小且显著更快。 在模拟过程中，计算密集型的参考MLFF仅在每$N$步进行评估，而轻量级的BoostMD模型则以极低的计算成本处理中间步骤。 我们的实验表明， BoostMD相比参考模型实现了八倍的速度提升，并能泛化到未见过的二肽。 此外，我们发现当运行分子动力学时，BoostMD能准确采样真实玻尔兹曼分布。 通过结合高效的特征复用与简化架构， BoostMD为进行大规模、长时间尺度的分子模拟提供了一个稳健的解决方案，使高精度的ML驱动建模更加易于访问和实用。 显示英文摘要

摘要： Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning force fields (MLFFs) have emerged as powerful tools that achieve near quantum mechanical accuracy, with promising generalization capabilities. However, their practical use is often limited by long inference times compared to classical force fields, especially when running extensive molecular dynamics (MD) simulations required for many biological applications. In this study, we introduce BoostMD, a surrogate model architecture designed to accelerate MD simulations. BoostMD leverages node features computed at previous time steps to predict energies and forces based on positional changes. This approach reduces the complexity of the learning task, allowing BoostMD to be both smaller and significantly faster than conventional MLFFs. During simulations, the computationally intensive reference MLFF is evaluated only every $N$ steps, while the lightweight BoostMD model handles the intermediate steps at a fraction of the computational cost. Our experiments demonstrate that BoostMD achieves an eight-fold speedup compared to the reference model and generalizes to unseen dipeptides. Furthermore, we find that BoostMD accurately samples the ground-truth Boltzmann distribution when running molecular dynamics. By combining efficient feature reuse with a streamlined architecture, BoostMD offers a robust solution for conducting large-scale, long-timescale molecular simulations, making high-accuracy ML-driven modeling more accessible and practical.