什么时候我们可以信任来自对分布函数测量的结构模型?
Phillip M Maffettone1, William J K Fletcher1, Thomas C Nicholas1
1Department of Chemistry, University of Oxford, Inorganic Chemistry Laboratory, South Parks Road, Oxford OX1 3QR, UK. andrew.goodwin@chem.ox.ac.uk.
Faraday discussions
|September 11, 2024
概括
模型概率有助于区分产生相同对分布函数 (PDF) 的类似结构模型. 这种方法有助于稳健的结构确定,即使使用有限的PDF数据,并建议机器学习.
科学领域:
- 材料科学 材料科学 材料科学
- 结构化学 结构化学
- 计算材料科学科学 计算材料科学
背景情况:
- 配对分布函数 (PDF) 对于描述复杂材料结构至关重要.
- 同型结构,即不同的模型产生相同的PDF,在结构分析中构成重大挑战.
研究的目的:
- 介绍和证明模型概率在同度结构解决方案之间进行区分的实用性.
- 在具有挑战性的结构确定场景中展示模型概率的应用.
主要方法:
- 使用模型概率作为区分等效PDF模型的一般框架.
- 将这种方法应用于两个案例研究:小和无形碳酸.
主要成果:
- 证明模型概率有效地区分同质结构.
- 在信息贫困的PDF场景中展示了模型概率的成功应用.
结论:
- 模型概率提供了一种可靠的方法来解决来自等价PDF文件的结构模糊性.
- 这些发现突显了机器学习在增强基于PDF的结构确定方面的潜力.
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