优化预测的化学键在接口动力学通过局部不确定性估计与神经网络合奏
Suman Bhasker-Ranganath1,2, Filippo Balzaretti1,2, Johannes Voss1
1SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.
Journal of chemical information and modeling
|January 27, 2026
概括
这项研究引入了一个数据效率高的框架,用于训练机器学习的原子间潜力. 它使用不确定性估计来改进界面化学和异质催化模型.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 准确的界面化学建模对于异质催化非常重要.
- 训练机器学习原子间潜力 (MLIP) 需要大量的数据,往往限制了它们的应用.
- 现有的方法很难量化复杂系统的预测可靠性.
研究的目的:
- 为培训MLIP进行界面化学开发一个数据效率高的框架.
- 为了使异质催化系统的MLIP中可靠的不确定性量化.
- 改进积极学习策略,以提高MLIP的准确性.
主要方法:
- 程序生成的原子几何 (散体,表面,气相) 和随机结构用于训练数据生成.
- 在不同的数据子集上训练的神经网络MLIP集,用于不确定性估计.
- 原子节点能量水平不确定性的量化,以确定模型的弱点.
主要成果:
- 在原子层面实现了可靠的不确定性估计.
- 不确定性估计成功地确定了-系统中的非物理结合和不足的,物理相关的相互作用 (例如,表面扩散,结合断裂/形成).
- 提出了基于局部不确定性的策略来检测异常值.
结论:
- 拟议的框架提高了MLIP用于界面化学的数据效率和可靠性.
- 不确定性量化是识别局限性和指导积极学习中进一步数据采集的关键.
- 这种方法提高了MLIPs在催化系统中模拟扩展级反应动态的准确性.
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