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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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全球通用缩放和超小参数化在机器学习中具有超线性原子间潜力的超小参数化.

Yanxiao Hu1, Ye Sheng1, Jing Huang1

  • 1State Key Laboratory of Quantum Functional Materials and Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.

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概括
此摘要是机器生成的。

我们开发了SUS2-MLIP,这是一种机器学习的原子间潜能模型,它包含了普遍的缩放规律. 这种方法提高了材料设计和模拟的模型通用性和可扩展性,即使数据有限.

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全球范围的扩展.原子间潜力是一个原子间潜力.机器学习是机器学习.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 人工智能的人工智能

背景情况:

  • 机器学习原子间潜力 (MLIP) 对于材料设计和模拟至关重要.
  • 目前的MLIP缺乏物理约束,导致域外挑战和普遍性差.
  • 可扩展性和物理相关性仍然是现有MLIP模型的关键限制.

研究的目的:

  • 开发一种具有增强通用性和可扩展性的机器学习原子间潜力 (MLIP) 模型.
  • 解决当前MLIP模型中固有的域外挑战.
  • 为材料模拟创建一个高效和物理知情的模型.

主要方法:

  • 整合了从普遍状态方程 (UEOS) 衍生的全球通用缩放法.
  • 开发了一种超小参数化的MLIP,命名为SUS2-MLIP,具有超线性表达能力.
  • 将元素空间与坐标空间脱,以减少模型参数.

主要成果:

  • SUS2-MLIP显示了显著减少的参数和固有的概括性和可扩展性.
  • 该模型通过辐射函数中的非线性嵌入转换表现出超线性表达能力.
  • 与最先进的MLIP模型相比,实现了更高的计算效率,特别是对于多元元素材料.

结论:

  • 通过整合物理约束,SUS2-MLIP提供了一个高效的通用MLIP模型.
  • 该模型克服了域外困难,提高了材料模拟中的通用性和可扩展性.
  • 这项工作为将物理定律纳入人工智能驱动的材料发现提供了一条途径.