Title: Shadow Molecular Dynamics for Flexible Multipole Models Show Chinese title

Title: 阴影分子动力学用于柔性多极模型

Abstract: Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy functions, potentials, and force terms, explicitly incorporating monopole-monopole, dipole-monopole, and dipole-dipole interactions. In our formulation, both atomic monopoles and atomic dipoles are treated as extended dynamical variables alongside the propagation of the nuclear degrees of freedom. We demonstrate that introducing the additional dipole degrees of freedom preserves the stability and accuracy previously seen in monopole-only shadow molecular dynamics simulations. Additionally, we present a shadow molecular dynamics scheme where the monopole charges are held fixed while the dipoles remain flexible. Our extended shadow dynamics provide a framework for stable, computationally efficient, and versatile molecular dynamics simulations involving long-range interactions between flexible multipoles. This is of particular interest in combination with modern artificial intelligence and machine learning techniques, which are increasingly used to develop physics-informed and data-driven foundation models for atomistic simulations. These models aim to provide transferable, high-accuracy representations of atomic interactions that are applicable across diverse sets of molecular systems, which requires accurate treatment of long-range charge interactions. Show Chinese abstract

Abstract: 阴影分子动力学为具有长程静电相互作用的柔性电荷模型提供了一个高效且稳定的原子模拟框架。 尽管之前的实现仅限于原子单极电荷分布，但我们将其扩展到柔性多极模型。 我们推导了阴影能量函数、势能和力项的详细表达式，明确包含了单极-单极、偶极-单极和偶极-偶极相互作用。 在我们的公式中，原子单极和原子偶极都被视为与核自由度传播一起的扩展动力学变量。 我们证明，引入额外的偶极自由度可以保持在仅含单极的阴影分子动力学模拟中之前观察到的稳定性和准确性。 此外，我们提出了一种阴影分子动力学方案，在该方案中，单极电荷被固定，而偶极则保持灵活。 我们扩展的阴影动力学提供了一个框架，用于涉及柔性多极之间长程相互作用的稳定、计算高效且通用的分子动力学模拟。 这在与现代人工智能和机器学习技术结合时尤为重要，这些技术正越来越多地用于开发物理信息和数据驱动的基础模型，用于原子模拟。 这些模型旨在提供可转移的、高精度的原子相互作用表示，适用于各种分子系统，这需要对长程电荷相互作用进行准确处理。