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CatEmbed:通过分类实体嵌入获得的机器学习表示,用于预测双金属合金表面上的吸附和反应能量

Clara Kirkvold¹, Brianna A Collins¹, Jason D Goodpaster¹

¹Department of Chemistry, University of Minnesota, Smith Hall, 207 Pleasant St SE, Minneapolis, Minnesota 55455-0431, United States.

The journal of physical chemistry letters

|June 24, 2024

概括

我们介绍了CatEmbed,一种新的机器学习方法,使用分类数据高精度预测双金属表面的吸附能. 这种方法也准确地预测了反应能量,推进了材料科学预测.

科学领域:

计算材料科学科学计算材料科学
机器学习在化学中的应用
表面科学和催化剂的研究

背景情况:

预测金属表面的吸附能量通常需要复杂的元素和几何描述符.
现有的机器学习模型经常与双金属合金表面的多样性作斗争.
材料科学的特征化方法可以通过来自其他领域的技术来增强,例如自然语言处理.

研究的目的:

开发一种新的机器学习方法,用于预测双金属合金表面上的吸附能.
为了利用分类实体嵌入来表示表面和吸附性质.
扩展对双金属系统反应能量的预测方法.

主要方法:

应用了基于自然语言处理的分类实体嵌入,以表示分类描述符 (表面组成,吸附物类型,站点类型).
通过将学习的分类特征与数值描述符 (例如,板块金属比率) 结合起来,开发了CatEmbed表示.
训练决策树模型使用CatEmbed表示来预测吸附和反应能量.

主要成果:

在没有使用明确的几何信息的情况下,CatEmbed模型实现了0.12 eV的平均绝对误差 (MAE) 来预测吸附能量.
扩展的CatEmbed-React表示实现了0.08 eV的MAE,用于预测双金属表面的反应能量.
证明了分类描述符的显著预测能力,当它们被有效地表现时.

结论:

分类实体嵌入是一种高效的特色化策略,用于机器学习在表面科学.
CatEmbed和CatEmbed-React表示方法提供了对双金属合金吸附和反应能量的准确预测.
这项工作为在催化和材料设计中使用简化输入特征进行数据驱动的发现开辟了新的途径.

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