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Trends in Lattice Energy: Ion Size and Charge02:54

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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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In classical mechanics, the two-body problem is one of the fundamental problems describing the motion of two interacting bodies under gravity or any other central force. When considering the motion of two bodies, one of the most important concepts is the reduced mass coordinates, a quantity that allows the two-body problem to be solved like a single-body problem. In these circumstances, it is assumed that a single body with reduced mass revolves around another body fixed in a position with an...
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低级矩阵和张量近似用于压缩机器学习的原子间潜力.

Igor Vorotnikov1, Fedor Romashov1, Nikita Rybin2,3

  • 1Faculty of Computer Science, HSE University, Pokrovsky Boulevard 11, Moscow 109028, Russian Federation.

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机器学习的原子间潜力 (MLIP) 现在可以使用低级因子分解压缩高达50%,显著提高计算效率,而不会牺牲材料科学模拟的准确性.

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 机器学习在物理学中的应用
  • 科学计算科学计算

背景情况:

  • 机器学习的原子间潜力 (MLIP) 与传统的力场相比,提供了更高的精度.
  • MLIP的灵活性依赖于描述局部原子环境的基础集.
  • 减少MLIP参数是有效模拟的关键.

研究的目的:

  • 开发和验证MLIPs的压缩方法.
  • 为了提高MLIP模拟的计算效率.
  • 探索压缩对潜在能量表面精度的影响.

主要方法:

  • 低等级矩阵和张量分解与固定等级约束.
  • 自动排名增强算法用于优化潜在的合适性.
  • 使用动量张量潜力 (MTP) 和原子集群扩展 (ACE) 进行验证.

主要成果:

  • 在没有精度损失的情况下,实现了MLIP的高达50%的压缩.
  • 在多元组件系统 (Mo-Nb-Ta-W合金,LiF-NaF-KF盐,甘氨酸晶体) 上证明了成功的压缩.
  • 在不同的MLIP模型中验证了压缩方法的普遍性.

结论:

  • 低级因子化为压缩MLIP提供了一种有效的方法.
  • 开发的方法提高了模拟效率,同时保持了预测能力.
  • 这种方法广泛适用于材料科学中的各种MLIP模型.