剩余补偿整体框架用于对合金表面的结合能高精度可解释的预测
Jiaqiang Yang1,2,3, Ningyu Zhang1, Yubing You1
1Zhongyuan Critical Metals Laboratory and School of Material Science and Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China.
The journal of physical chemistry letters
|February 5, 2026
概括
我们开发了一个新的框架,将SISSO和随机森林回归 (RFR) 结合起来,以准确预测约束能量. 这种方法平衡了预测准确性和模型可解释性,有助于催化剂的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 准确的结合能量的预测对于催化剂设计至关重要.
- 高维模型提供了准确性,但缺乏可解释性,阻碍了机械的理解.
- 平衡预测准确性和模型可解释性是一个关键的挑战.
研究的目的:
- 开发一种用于预测原子物种 (C,N,O) 结合能量的新框架.
- 在预测准确性和模型可解释性之间实现平衡.
- 通过理解结构-性能关系来加速催化剂的发现.
主要方法:
- 开发了一个可解释的SISSO (同时空间光谱分析) 模型,具有五个描述符.
- 使用描述符来识别具有强烈吸附剂亲和力的表面元素.
- 采用随机森林回归 (RFR) 模型来弥补SISSO预测残余.
- 将SISSO和RFR整合到一个协同作用的SISSO-RFR框架中.
主要成果:
- SISSO模型强调了具有强烈吸附剂亲和力的表面元素的重要性.
- 与传统组合和双SISSO模型相比,SISSO-RFR框架实现了显著更高的预测准确性.
- 该框架成功地平衡了模型透明度与预测能力.
结论:
- 剩余补偿的SISSO-RFR框架为具有约束力的能源预测提供了有效的解决方案.
- 这种方法增强了对催化中的结构-性能关系的理解.
- 该框架通过提高准确性和可解释性来简化催化剂发现.
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