基于对分布函数预测和分析高缩合金上的酸盐吸附,使用混合机器学习框架
Truong Nhut Huynh1, Xiang He1, Kim-Doang Nguyen1
1Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, Florida 32901, United States.
Journal of chemical information and modeling
|November 26, 2025
概括
机器学习模型可以使用对分布函数 (PDF) 数据预测高合金 (HEA) 催化剂性能. 这种方法优化了化学转换的催化剂设计.
科学领域:
- 材料科学 材料科学 材料科学
- 催化剂是一种催化剂.
- 计算化学计算化学
背景情况:
- 高合金 (HEA) 具有特殊的稳定性和电子特性,使其成为有前途的催化剂.
- 高能电机的复杂设计空间需要机器学习来优化催化性能.
- 结构特征对于HEA催化剂开发中的机器学习准确性至关重要.
研究的目的:
- 研究使用对分布函数 (PDF) 数据作为输入特征用于基于机器学习的HEA催化剂优化.
- 开发一种混合机器学习框架,用于使用PDF数据预测催化活性.
- 与传统的机器学习算法相比,评估这个框架的性能.
主要方法:
- 使用主要组件分析 (PCA) 来减少PDF数据的维度.
- 采用了混合框架,结合了基于变压器的模型和大型语言模型 (LLM).
- 该框架预测了FeCoNiCuZn HEA表面酸盐吸附的吉布斯自由能量.
主要成果:
- 混合框架使用PCA减少的PDF数据准确地预测了酸盐吸附的吉布斯自由能量.
- 性能明显超过了传统的算法,如随机森林,支持向量回归和梯度增强.
- 通过LLM集成,进一步提高了预测准确性,并提供了可解释的见解.
结论:
- 通过PCA缩小的PDF数据可以作为HEA催化剂设计中的机器学习模型的有效输入功能.
- 开发的混合变压器-LLM框架能够准确预测和优化HEA催化剂.
- 这种方法促进了基于HEA的催化剂的预测设计,提高了活性和选择性.
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