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使用机器学习与原子表面和当地的水特征来预测异构的冰核化

Abhishek Soni¹, G N Patey¹

¹Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.

The Journal of chemical physics

|March 26, 2024

概括

机器学习和分子动力学模拟预测异质冰核形成 (HIN) 的可能性. 表面附近的局部水特性是关键预测因素,指导冰核粒子 (INP) 的设计.

科学领域:

物理化学物理化学
材料科学材料科学材料科学
气候科学气候科学

背景情况:

异质冰核化 (HIN) 对气候,纳米技术和冷保存至关重要.
鉴定有效的冰核粒子 (INPs) 是由于实验显微镜分辨率的局限性而具有挑战性.
了解INP疗效的分子级决定因素是一个基本的科学问题.

研究的目的:

使用分子动力学 (MD) 和机器学习 (ML) 开发异质冰核化 (HIN) 的预测模型.
确定控制冰核形成倾向的表面和周围水的关键分子特征.
为了克服传统的MD模拟的计算成本限制,用于观察HIN.

主要方法:

采用MD模拟来生成153个不同表面的原子数据及其与水的相互作用.
在核化开始之前,从短时间的MD模拟 (≤300 ns) 中提取了当地的水特征.
训练并评估了三个ML分类模型 (随机森林,支持向量机,高斯过程) 来预测HIN的可能性.

主要成果:

机器学习模型在预测HIN概率方面实现了高准确度 (0.89 ± 0.05对于随机森林).
发现当地的水特征,如类似冰的结构和密度/极化概况,对于准确的预测至关重要.
仅包括局部水特征提供了与使用表面和水特征模型相比的准确性,表明局部水结构封装了表面影响.

结论:

这种结合的MD-ML方法有效地预测了异质冰核化倾向.
表面附近的局部水结构是冰核形成的关键决定因素,简化了有效INP的搜索.
这些发现为设计用于各种应用的新材料提供了框架,这些新材料可以促进或抑制冰核形成.

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