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Updated: Jan 17, 2026

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机器学习时代的不确定性用于原子模型
Federico Grasselli1,2, Sanggyu Chong3, Venkat Kapil4,5,6
1Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università degli Studi di Modena e Reggio Emilia 41125 Modena Italy federico.grasselli@unimore.it.
概括
机器学习替代模型增强了原子模型,但引入了不确定性. 本视角审查了不确定性量化方法及其对科学研究中的模型可靠性,准确性和稳定性的影响.
科学领域:
- 计算材料科学 计算材料科学
- 数据驱动的建模.
背景情况:
- 机器学习替代模型越来越多地用于原子模型,以提高效率和探索复杂的系统.
- 这些模型的数据驱动性需要强大的方法来量化和管理预测不确定性.
研究的目的:
- 提供适用于原子模型的最先进的不确定性估计技术的概述.
- 检查模型准确性,不确定性和数据特征之间的关键关系.
主要方法:
- 对贝叶斯框架和组合技术进行不确定性定量化的审查.
- 分析模型性能,训练数据和模型稳定性之间的相互作用.
主要成果:
- 不确定性量化对于在原子模拟中可靠的机器学习预测至关重要.
- 数据集的组成和获取策略显著影响模型的准确性和可转移性.
结论:
- 有效地管理不确定性对于机器学习在原子模型中的可靠应用至关重要.
- 需要进一步的研究来解决正在进行的关于模型可转移性和稳定性的辩论.
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