Bellini Giulia, Koch Gregor, Girgsdies Frank, Dong Jinhu, Carey Spencer J, Timpe Olaf, Auffermann Gudrun, Scheffler Matthias, Schlögl Robert, Foppa Lucas, Trunschke Annette
Inorganic Chemistry Department, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195, Berlin, Germany.
Max-Planck-Institut für Chemische Physik Fester Stoffe, Nöthnitzer Straße 40, 01187, Dresden, Germany.
Angew Chem Int Ed Engl. 2025 Feb 3;64(6):e202417812. doi: 10.1002/anie.202417812. Epub 2024 Nov 21.
The identification of key materials' parameters correlated with the performance can accelerate the development of heterogeneous catalysts and unveil the relevant underlying physical processes. However, the analysis of correlations is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant. Here, we tackle these challenges for the CO oxidation catalyzed by perovskites using a combination of rigorous experiments and artificial intelligence. A series of 13 ABO (A=La, Pr, Nd, Sm; B=Cr, Mn, Fe, Co) perovskites was synthesized, characterized, and tested in catalysis. To the resulting dataset, we applied the symbolic-regression SISSO approach. We identified an analytical expression correlated with the activity that contains the normalized unit-cell volume, the Pauling electronegativity of the elements A and B, and the ionization energy of the element B. Therefore, the activity is described by crystallographic distortions and by the chemical nature of A and B elements. The generalizability of the identified descriptor is confirmed by the good quality of the predictions for 3 additional ABO and 16 chemically more complex AMnB'O (A=La, Pr, Nd; B'=Fe, Co, Ni, Cu, Zn) perovskites.
识别与性能相关的关键材料参数可以加速多相催化剂的开发，并揭示相关的潜在物理过程。然而，相关性分析常常受到数据不一致的阻碍。此外，重要但未知的关系可能很重要，并且各种过程的复杂性可能很显著。在这里，我们使用严格的实验和人工智能相结合的方法来应对钙钛矿催化CO氧化的这些挑战。合成了一系列13种ABO（A = La、Pr、Nd、Sm；B = Cr、Mn、Fe、Co）钙钛矿，对其进行了表征并测试了催化性能。对于所得数据集，我们应用了符号回归SISSO方法。我们确定了一个与活性相关的解析表达式，该表达式包含归一化晶胞体积、元素A和B的鲍林电负性以及元素B的电离能。因此，活性由晶体学畸变以及A和B元素的化学性质来描述。通过对另外3种ABO和16种化学性质更复杂的AMnB'O（A = La、Pr、Nd；B' = Fe、Co、Ni、Cu、Zn）钙钛矿的良好预测质量，证实了所确定描述符的通用性。
