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An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
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用于机器学习的数据生成 - 原子间潜力和超越

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概括
此摘要是机器生成的。

高质量的训练数据对于化学中可靠的机器学习模型至关重要. 本综述探讨了创建有效数据集的方法,以提高模型性能和适用性.

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科学领域:

  • 数据驱动的化学.
  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.

背景情况:

  • 机器学习模型正在彻底改变分子性质预测.
  • 基于ML的原子间潜力 (MLIP) 能够进行精确的原子级模拟.
  • 培训数据质量是MLIP可靠性的主要因素.

研究的目的:

  • 审查MLIP培训数据的基本组成部分和完整性.
  • 讨论确保模型可扩展性和可转移性的方法.
  • 突出构建特定领域培训集的策略.

主要方法:

  • 积极学习策略和实施.
  • 对于原子化数据采集的不确定性量化.
  • 使用改造和替代潜在能量表面获取数据.
  • 原子数据采样者的作用.

主要成果:

  • 积极学习和不确定性量化增强了数据采集.
  • 使用专门的数据采样器生成各种结构.
  • 新的方法提高了培训数据的多样性.
  • 列出了涵盖关键化学空间的公开数据集.

结论:

  • 有效的训练数据构建对于推进数据驱动化学至关重要.
  • 讨论的方法提高了MLIP的可靠性和适用性.
  • 该审查为研究人员在构建强大的ML模型方面提供了一份指南.