机器学习在原子精确金纳米集群的边界轨道能量
Tingting Jiang1, Qiqi Zhang1, Zhan Si1
1Department of Chemistry and Centre for Atomic Engineering of Advanced Materials, Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education, Institutes of Physical Science and Information Technology and Anhui Province Key Laboratory of Chemistry for Inorganic/Organic Hybrid Functionalized Materials, Anhui University, Hefei, Anhui 230601, China.
The Journal of chemical physics
|October 28, 2025
概括
一个新的机器学习模型准确地预测金纳米集群 (Au NC) 的电子状态. 这种经济高效的方法使用关键的结构描述符,使电化学和光化学应用的催化剂的开发速度更快.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 纳米技术和催化剂
背景情况:
- 原子精确的金纳米集群 (AuNCs) 在催化中显示出巨大的潜力.
- 准确预测它们的电子状态至关重要,但在计算上昂贵.
- 由于高的实验和计算成本,现有的方法面临挑战.
研究的目的:
- 开发一个具有成本效益的机器学习 (ML) 模型,用于预测Au NCs的电子状态.
- 确定管理Au NCs电子属性的关键结构描述符.
- 为了能够准确地预测最高占用分子轨道 (HOMO),最低不占用分子轨道 (LUMO),HOMO-LUMO间隙和氧化潜力 (OP).
主要方法:
- 开发了一个ML模型,利用可解释的自动化特征工程.
- 使用双向渐进回归和Kolmogorov-Arnold网络模型选了227个候选参数.
- 在79个数据点上训练模型,并在20个数据点上验证,仅使用4个关键描述符.
主要成果:
- 在测试组中,达到的平均平均误差 (MAE) 为0.17 eV (HOMO),0.27 eV (LUMO) 和0.16 eV (HOMO-LUMO差距).
- 预测的氧化电位 (OP) 与MAE为0.20V.
- 确定了集群收费数 (NC) 和平均 Au-Au 协调数 (CNAu-Au) 作为关键描述符.
结论:
- 一个小组的关键结构描述器可以准确和经济高效地预测Au NC电子结构.
- 开发的ML模型为加速在催化中AuNC的设计和应用提供了有价值的工具.
- 这种方法显著降低了与 Au NC 描述相关的计算和实验负担.
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