可解释的ML-DFT框架用于性能预测和结构-活动关系分析酸性铜平衡器的酸性铜平衡器
Bo Yang1, Wenmin Liao1, Yue Kong1
1College of Materials Engineering, North China Institute of Aerospace Engineering, Langfang 065000, PR China.
Journal of chemical information and modeling
|March 14, 2026
概括
本研究介绍了一种机器学习 (ML) 和密度函数理论 (DFT) 框架,以快速发现新的铜电平衡器. 确定了五种优越的平衡器,其中一个超出了工业标准.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 电化学 电化学 电化学
背景情况:
- 高性能等级器对于微电子铜电是必不可少的.
- 目前的发展受到昂贵的实验查和不太了解结构-活动关系 (SAR) 的限制.
研究的目的:
- 开发一个集成的机器学习 (ML) 和密度函数理论 (DFT) 框架,以快速发现新的电平衡器.
- 为了确定关键的分子描述器,规范平衡的有效性.
主要方法:
- 使用实验性溶解峰值减少量 (DPDA) 和DFT计算的吸附能量 (Eads) 建立了一个数据集.
- 采用ML模型 (XGBoost回归,CART) 来预测521个预先选的分子的DPDA和Eads.
- 利用皮尔森相关性和SHAP分析来确定关键的分子描述符.
主要成果:
- 成功地预测了大量分子的DPDA和Eads,确定了五种优越的新型水平器 (Res-1到Res-5).
- 与工业标准Janus Green B (JGB) 相比,Res-5的性能明显优越.
- 确定了电友分子骨干和N-N功能组相互作用,这些相互作用对于平衡疗效至关重要.
结论:
- 集成的ML-DFT框架为高性能水平器提供了有效的选工具.
- 这项研究为电化学接口的分子SAR提供了新的见解.
- 发现了具有潜在工业应用的新型平面机.
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