基于模型自身理解的基础模型的 Δ-模型校正
Mads-Peter Verner Christiansen1, Bjørk Hammer1
1Department of Physics and Astronomy, Center for Interstellar Catalysis, Aarhus University, DK-8000 Aarhus C, Denmark.
The Journal of chemical physics
|May 8, 2025
概括
原子间潜力的基础模型需要针对特定材料进行微调. 本研究介绍了一种使用高斯过程回归 (GPR) 的 Δ-学习方法,以增强通用潜力,提高铜氧化物和硫原子等材料的精度.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 通用原子间电位或基础模型具有广泛的适用性,但可能需要针对特定材料子类进行调整.
- 像CHGNet这样的现有模型虽然强大,但在训练集中遇到数据稀缺时可能会出现局限性,特别是在新型或代表性不足的原子环境中.
研究的目的:
- 开发和验证一个 Δ-学习增强方案,用于提炼通用原子间潜能.
- 解决基础模型在准确描述特定材料系统方面的局限性,例如超薄氧化物薄膜和金属硫接口.
主要方法:
- 实施基于高斯过程回归 (GPR) 的 Δ 模型用于剩余校正.
- 探索各种聚合策略 (全球,物种分离,原子) 用于 Δ 模型中的表示向量.
- 增强的CHGNet模型对氧化铜和硫系统的应用和评估.
主要成果:
- 增强的CHGNet模型准确地预测了"8"氧化的能量,超过了以前基于密度函数理论的预测.
- Δ模型有效地纠正了CHGNet对Cu{111},Ag{111}和Au{111}上的硫原子覆盖层的描述中的错误.
- 纠正的必要性与训练数据中金属硫环境的稀缺性有关,导致过度依赖硫-硫相互作用.
结论:
- Δ学习提供了一种有效的方法来增强通用原子间潜力,提高它们对特定材料子类的预测准确度.
- 基于GPR的 Δ 模型利用基础模型固有的表示,提供了一种可概括的方法来解决数据限制.
- 这项工作强调了增强方案的必要性,以在各种材料中强有力的应用通用潜力模型.
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