线性标度的无势数据驱动分子动力学用于任意大小的水簇$(\ ext{H}_2\ ext{O})_n$
Linear-Scaling Potential-Free Data-Driven Molecular Dynamics for Arbitrary-Sized Water Clusters $(\text{H}_2\text{O})_n$
AI总结：
本文提出一种线性标度的无势数据驱动分子动力学框架，用于预测任意大小的水簇能量和原子力，通过高维等变特征和自洽训练方法，实现比DeepMD更高的精度和效率。
摘要：
传统分子动力学模拟方法如从头计算分子动力学（AIMD）和经验力场分子动力学（EFFMD）在物理精度和计算效率之间存在显著权衡。本文提出了一种线性标度的无势数据驱动分子动力学（PDMD）框架，用于预测任意大小的水簇$(\ ext{H}_2\ ext{O})_n$的系统能量和原子力。具体而言，PDMD采用基于高斯的原子几何描述符生成高维等变特征，然后利用ChemGNN，一种能够自适应学习原子化学环境的图神经网络模型，而无需先验知识。通过迭代自洽训练方法，收敛的PDMD实现了每原子1.39 meV的能量均方误差和每埃50.7 meV的力均方误差，比DeepMD在能量精度上提高约5倍，在力精度上提高约3倍。因此，线性标度的PDMD可以在计算成本低多个数量级的情况下重现水簇的AIMD性质，如对包含数千或更多分子的系统进行模拟。这些结果表明，所提出的PDMD具有多相预测能力，能够在保持AIMD级精度的同时实现超快、通用的分子动力学模拟。该精度是通过高效捕捉许多体势能，这些势能在许多多原子系统中至关重要，但在EFFMD中通常缺失而实现的。此外，我们构建了一个包含超过300,000个$(\ ext{H}_2\ ext{O})_n$结构的从头计算数据集，标准化在统一的PyTorch Geometric框架中，以支持可扩展评估分子动力学的人工智能方法。
英文摘要：
Conventional molecular dynamics (MD) simulation approaches, such as $\textit{ab initio}$ MD (AIMD) and empirical force field MD (EFFMD), face significant trade-offs between physical accuracy and computational efficiency. This work presents a linear-scaling potential-free data-driven molecular dynamics (PDMD) framework for predicting system energy and atomic forces of arbitrary-sized water clusters $(\text{H}_2\text{O})_n$. Specifically, PDMD employs a Gaussian-based atomic geometry descriptor to generate high-dimensional, atomistic footprints, then leverages ChemGNN, a graph neural network model that adaptively learns the atomic chemical environments without requiring $\textit{a priori}$ knowledge. Through an iterative self-consistent training approach, the converged PDMD achieves a mean absolute error of 1.39 meV/atom for energy, outperforming other state-of-the-art models such as DeepMD, MACE, NequIP, and SevenNet by at least 2.6x in accuracy with the same dataset. As a result, the linear-scaling PDMD can reproduce the AIMD properties of water clusters at orders-of-magnitude lower computational cost, as illustrated by simulations of systems consisting of thousands or more molecules. These results demonstrate that the proposed PDMD offers multiphase predictive power and enables ultra-fast, general-purpose MD simulations while retaining AIMD-level accuracy. This accuracy is achieved by efficiently capturing many-body potentials that are critical in numerous polyatomic systems but are often missing in EFFMD. Moreover, we have constructed an $\textit{ab initio}$ dataset with over 300,000 $(\text{H}_2\text{O})_n$ structures, standardized in a unified PyTorch Geometric framework, to support scalable evaluation of artificial intelligence methods for molecular dynamics.
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