原子吸附能量的预测对双金属过渡金属表面使用可解释的机器学习加速密度函数理论方法.
Jan Goran T Tomacruz1, Michael T Castro1, Miguel Francisco M Remolona2
1Laboratory of Electrochemical Engineering, Department of Chemical Engineering, University of the Philippines Diliman, Quezon City, Metro Manila, 1101, Philippines.
机器学习模型准确地预测了过渡金属的吸附物结合能. 确定了影响吸附的关键特征,与已建立的催化应用的表面科学模型保持一致.
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
- 表面科学是一门学科.
背景情况:
- 在催化剂设计中,预测过渡金属 (TM) 表面的吸附剂吸附能量至关重要.
- 密度函数理论 (DFT) 的计算对于大规模选而言是计算上昂贵的.
- 机器学习 (ML) 为加速这些预测提供了一个有希望的途径.
研究的目的:
- 识别关键特征和属性趋势,预测TM表面上碳,和氧的吸附能量.
- 开发和解释准确的ML模型用于吸附能量的预测.
- 为TM表面研究建立一个可解释的ML-DFT方法.
主要方法:
- 通过使用DFT计算,从26个单金属和400个双金属fcc(111) TM表面生成数据集.
- 提取了十四个元素,电子和结构性质.
- 采用特征选择和应用随机森林回归 (RFR),高斯过程回归 (GPR) 和人工神经网络 (ANN) 模型.
- 使用了模型不可知解释方法,如变特征重要性 (PFI) 和夏普利添加式扩展 (SHAP).
主要成果:
- 在所有数据集中,RFR和GPR模型实现了最高的预测准确度.
- 解释方法确定了与已建立的TM结构-属性-性能关系 (例如,d带模型,弗里德尔模型) 一致的关键特征和方向趋势.
- 发现较高折叠的吸附部位是显著的预测因素.
结论:
- 可解释的ML-DFT方法有效地预测TM上的原子吸附能量.
- 这种方法提高了模型的可解释性,为吸附机制提供了洞察力.
- 该方法适用于TM和其衍生品,用于催化加速材料发现.
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