Mauß Jonathan M, Kley Klara S, Khobragade Rohini, Tran Nguyen-Khang, de Bellis Jacopo, Schüth Ferdi, Scheffler Matthias, Foppa Lucas
Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, Mülheim an der Ruhr 45470, Germany.
The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, Berlin 14195, Germany.
ACS Catal. 2025 Jul 11;15(15):12652-12665. doi: 10.1021/acscatal.5c02226. eCollection 2025 Aug 1.
Describing heterogeneous catalysis is complicated by the intricate interplay of processes that govern catalyst performance. The evolving chemical environment and the kinetics of catalyst's structural changes during reactions often lead to unknown local geometries and chemistry, which can shift reactivity over time. Here, we perform systematic experiments and apply a focused artificial-intelligence (AI) approach to model the measured time-on-stream-dependent reactivity of palladium-based bimetallic catalysts. These materials are synthesized via mechanochemistry and applied in the selective hydrogenation of concentrated acetylene streams(>14.0 vol %)under industrially relevant pressures (10 bar), resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Unlike the well-established hydrogenation of diluted acetylene (0.1 to 2.0 vol %) streams of naphtha steam cracking, the hydrogenation of concentrated acetylene streams remains largely underexplored due to the harsh reaction conditions and the explosive nature of acetylene. This precludes characterization or atomistic simulations to investigate catalyst time-on-stream behavior under realistic conditions. Our AI approach first uses subgroup discovery to identify descriptions of materials and reaction conditions resulting in noticeable acetylene conversion. Then, it models time-dependent selectivity focused on high acetylene conversion via the sure-independence-screening-and-sparsifying operator symbolic-regression approach. AI identifies key experimental and theoretical physicochemical descriptive parameters correlated with the reactivity, which highlight the critical interplay between the material structure and the chemical potential of the reaction mixture. The AI models enable the design of bimetallic and trimetallic catalysts, which are experimentally validated.
描述多相催化作用很复杂，因为决定催化剂性能的各种过程之间存在着错综复杂的相互作用。反应过程中不断变化的化学环境以及催化剂结构变化的动力学，常常导致局部几何形状和化学性质不明，这可能会使反应活性随时间发生变化。在此，我们进行了系统实验，并应用一种有针对性的人工智能（AI）方法，对基于钯的双金属催化剂的随反应时间变化的实测反应活性进行建模。这些材料是通过机械化学合成的，并应用于在工业相关压力（10巴）下对浓缩乙炔流（>14.0体积%）进行选择性加氢，该浓缩乙炔流源自一种假设的电等离子体辅助甲烷制乙烯过程。与石脑油蒸汽裂解中稀乙炔流（0.1至2.0体积%）的成熟加氢不同，由于反应条件苛刻以及乙炔的爆炸性，浓缩乙炔流的加氢在很大程度上仍未得到充分研究。这使得在实际条件下研究催化剂随反应时间的行为的表征或原子模拟无法进行。我们的人工智能方法首先使用子群发现来识别导致显著乙炔转化率的材料和反应条件的描述。然后，它通过确定独立性筛选和稀疏化算子符号回归方法，对专注于高乙炔转化率的随时间变化的选择性进行建模。人工智能识别出与反应活性相关的关键实验和理论物理化学描述参数，这些参数突出了材料结构与反应混合物化学势之间的关键相互作用。人工智能模型能够设计出双金属和三金属催化剂，并通过实验进行了验证。
