Ohba Nobuko, Yokoya Takuro, Kajita Seiji, Takechi Kensuke
Toyota Central R&D Labs., Inc. Nagakute Aichi 480-1192 Japan
Toyota Motor Corporation Higashi-Fuji Technical Center Susono Shizuoka 410-1193 Japan.
RSC Adv. 2019 Dec 17;9(71):41811-41816. doi: 10.1039/c9ra09886k. eCollection 2019 Dec 13.
Oxygen storage materials (OSMs), such as pyrochlore type CeO-ZrO (p-CZ), are used as a catalyst support for three-way catalysts in automotive emission control systems. They have oxygen storage capacity (OSC), which is the ability to release and store oxygen reversibly by the fluctuation of cation oxidation states depending on the reducing or oxidizing atmosphere. In this study, we explore high-capacity OSMs by using materials informatics (MI) combining experiments, first-principles calculations, and machine learning (ML). To generate training data for the ML model, the OSC values of 60 metal oxides were measured from the amount of CO produced under alternating flow gas between oxidizing (O) and reducing (CO) conditions at 973, 773, and 573 K. Descriptors were computed by atomic properties and first-principles calculations on each oxide. The support vector machine regression model was trained to predict the OSC at each temperature. The features describing OSC were automatically selected using grid search to achieve practical cross validation performance. The features related to the stability of the oxygen atoms in the crystal and the crystal structure itself such as cohesive energy are highly correlated with OSC. The present model predicts the OSC of 1300 existing oxides. Based on its high predictive power for OSC and synthesizability, we focused on CuNbO. We synthesized this material and experimentally confirmed that CuNbO showed a higher OSC than conventional OSM p-CZ. This MI scheme can significantly accelerate the development of new OSMs.
氧存储材料（OSMs），如烧绿石型CeO-ZrO（p-CZ），被用作汽车排放控制系统中三元催化剂的催化剂载体。它们具有氧存储容量（OSC），即根据还原或氧化气氛通过阳离子氧化态的波动可逆地释放和存储氧的能力。在本研究中，我们通过结合实验、第一性原理计算和机器学习（ML）的材料信息学（MI）来探索高容量的OSMs。为了生成ML模型的训练数据，在973K、773K和573K的氧化（O）和还原（CO）交替流动气体条件下，从产生的CO量测量了60种金属氧化物的OSC值。通过对每种氧化物的原子性质和第一性原理计算来计算描述符。训练支持向量机回归模型以预测每个温度下的OSC。使用网格搜索自动选择描述OSC的特征，以实现实际的交叉验证性能。与晶体中氧原子的稳定性和晶体结构本身（如内聚能）相关的特征与OSC高度相关。本模型预测了1300种现有氧化物的OSC。基于其对OSC的高预测能力和可合成性，我们重点关注了CuNbO。我们合成了这种材料，并通过实验证实CuNbO的OSC高于传统的OSM p-CZ。这种MI方案可以显著加速新型OSMs的开发。
