arXiv:2602.14867cond-mat.mtrl-scics.LG2026-02

用类原子单元实现原子与连续体协同模拟,速度快且精度高。

Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids

  • 用可变尺寸的类原子单元模拟非关键区域,匹配原子材料弹性特性。
  • 相比全原子模拟速度提升数十倍,误差小于1%。
  • 适合大尺度动力学模拟,尤其适用于裂纹、接触等复杂场景。

本文提出一种新型混合方法,可在关键区域(如接触面、裂纹区)进行原子级模拟,同时在其他区域采用连续介质建模。连续部分以不同尺寸的类原子单元表示,构成复合介质。通过优化类原子间相互作用势参数,使其弹性性能与原子体系一致。该优化方法在概念上类似在线机器学习,计算效率极高。该方法可直接集成至标准分子动力学软件(如LAMMPS),并已应用于基于简谐对势(Lennard-Jones)和多体键合势(Tersoff)的系统。模拟粒子碰撞过程显示,新方法在保持全原子模拟精度的同时,计算速度显著提升。与最近的AtC方法相比,本方法在计算效率和实现便捷性上均有明显优势。未来还可扩展用于其他物理现象建模。

原文摘要 · Abstract (English)

We report a novel hybrid method of simultaneous atomistic simulation of solids in critical regions (contacts surfaces, cracks areas, etc.), along with continuum modeling of other parts. The continuum is treated in terms of quasi-atoms of different size, comprising composite medium. The parameters of interaction potential between the quasi-atoms are optimized to match elastic properties of the composite medium to those of the atomic one. The optimization method coincides conceptually with the online Machine Learning (ML) methods, making it computationally very efficient. Such an approach allows a straightforward application of standard software packages for molecular dynamics (MD), supplemented by the ML-based optimizer. The new method is applied to model systems with a simple, pairwise Lennard-Jones potential, as well with multi-body Tersoff potential, describing covalent bonds. Using LAMMPS software we simulate collision of particles of different size. Comparing simulation results, obtained by the novel method, with full-atomic simulations, we demonstrate its accuracy, validity and overwhelming superiority in computational speed. Furthermore, we compare our method with other hybrid methods, specifically, with the closest one -- AtC (Atomic to Continuum) method. We demonstrate a significant superiority of our approach in computational speed and implementation convenience. Finally, we discuss a possible extension of the method for modeling other phenomena.

多尺度模拟分子动力学机器学习类原子

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