使用基于原子图的分类器加速结构搜索
Andreas Møller Slavensky1, Bjørk Hammer1
1Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Aarhus C DK-8000, Denmark.
The Journal of chemical physics
|July 3, 2024
概括
我们使用光谱图理论和沃罗诺伊拼图开发了一个新的原子学分类器. 这个工具有效地过原子结构,通过防止停滞来改善全球优化搜索.
科学领域:
- 计算材料科学 计算材料科学
- 化学物理 化学物理
- 数据分析数据分析
背景情况:
- 对原子结构的大数据集的分析在计算上具有挑战性.
- 识别来自潜在能量表面的独特结构配置对于材料发现至关重要.
- 全球优化算法可能会在局部最小值中停滞不前,阻碍对能源格局的有效探索.
研究的目的:
- 引入一种新的原子学分类器来区分原子结构.
- 将此分类器集成到全球优化算法中,以提高搜索效率.
- 为了证明分类器在解决复杂的全球优化问题的有效性.
主要方法:
- 开发了一个原子分类器,结合了光谱图理论和沃罗诺伊模块化.
- 将分类器作为过机制集成到全球优化与第一原则能量表达式 (GOFEE) 算法中.
- 将该方法应用于各种系统的全球优化问题,包括纳米粒子和晶体结构.
主要成果:
- 原子分类器成功地区分了不同潜在能量表面最小值的结构.
- 将其纳入GOFEE有效地过了探索地区的结构,减少了搜索停滞.
- 该方法有效地解决了2D,3D,Au12和LJ55/LJ75纳米粒子的全球优化问题.
结论:
- 开发的原子分类器是分析和排序大型原子数据集的宝贵工具.
- 将这个分类器集成到全球优化中,可以显著提高探索潜在能源景观的效率和稳定性.
- 这种方法为加速材料发现和理解复杂的原子系统提供了一个有希望的策略.
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