Discovery of Electronic Structure and Interfacial Interaction Features in Catalytic Activity

Digital Descriptors in Predicting Catalysis Reaction Efficiency and Selectivity

Accurately controlling the interactions and dynamic changes between multiple active sites (e.g., metals, vacancies, and lone pairs of heteroatoms) to achieve efficient catalytic performance is a key issue and challenge in the design of complex catalytic reactions involving 2D metal-supported catalysts, metal-zeolites, metal-organic catalysts, and metalloenzymes. With the aid of machine learning (ML), descriptors play a central role in optimizing the electrochemical performance of catalysts, elucidating the essence of catalytic activity, and predicting more efficient catalysts, thereby avoiding time-consuming trial-and-error processes. Three kinds of descriptors─active center descriptors, interfacial descriptors, and reaction pathway descriptors─are crucial for understanding and designing metal-supported catalysts. Specifically, vacancies, as active sites, synergize with metals to significantly promote the reduction reactions of energy-relevant small molecules. By combining some physical descriptors, interpretable descriptors can be constructed to evaluate catalytic performance. Future development of descriptors and ML models faces the challenge of constructing descriptors for vacancies in multicatalysis systems to rationally design the activity, selectivity, and stability of catalysts. Utilization of generative artificial intelligence and multimodal ML to automatically extract descriptors would accelerate the exploration of dynamic reaction mechanisms. The transferable descriptors from metal-supported catalysts to artificial metalloenzymes provide innovative solutions for energy conversion and environmental protection.

Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N2 and CO2 Reduction Reactions

The applications of machine learning (ML) in complex interfacial interactions are hindered by the time-consuming process of manual feature selection and model construction. An automated ML program was implemented with four subsequent steps: data distribution analysis, dimensionality reduction and clustering, feature selection, and model optimization. Without the need of manual intervention, the descriptors of metal charge variance (ΔQCT) and electronegativity of substrate (χsub) and metal (δχM) were raised up with good performance in predicting electrochemical reaction energies for both nitrogen reduction reaction (NRR) and CO2 reduction reaction (CO2RR) on metal-zeolites and MoS2 surfaces. The important role of interfacial interactions in tuning the catalytic reactivity in NRR and CO2RR was highlighted from SHAP analysis. It was proposed that Fe-, Cr-, Zn-, Nb-, and Ta-zeolites are favorable catalysts for NRR, while Ni-zeolite showed a preference for CO2RR. An elongated bond of N2 or a bent configuration of CO2 was shown in V-, Co-, and Mo-zeolites, indicating that the molecule could be activated after the adsorption in both NRR and CO2RR pathways. The generalizability of the automatically built ML model is demonstrated from applications to other catalytic systems such as metal-organic frameworks and SiO2 surfaces. The automated ML program is a useful tool to accelerate the data-driven exploration of relationship between structures and material properties without the need of manual feature selection.

Linearly Scaling Molecular Dynamic Modeling To Simulate Picosecond Laser Ablation of a Silicon Carbide Crystal

A molecular dynamics model for picosecond laser ablation of nanoscale silicon carbide crystals was established by linearly scaling the laser focal diameter, and the correlation between the molecular dynamic simulation of the nanoscale and the experimental reproduction of the microscale was achieved. The calculation accuracy of the molecular dynamic model was verified by ablating the surface of silicon carbide wafers with a laser pulse width of 37 ps. On this basis, this paper further investigated the influence of the laser pulse width and fluence on the surface ablation damage and modification width, threshold, and lattice temperature. The results showed that, when the laser pulse width is higher than 10 ps, the silicon carbide damage threshold increases with increasing the pulse width, while the modification threshold is almost unaffected by the pulse width. In addition, the influence of crystal orientation has been studied, and laser irradiation along the [1-100] crystal orientation induces a higher peak temperature, larger damage, and modification width and threshold, followed by irradiation along the [0001] crystal orientation and lowest along the [11-20] crystal orientation. Finally, with the linear scaling value increasing, the spatial distribution of the laser energy field deviates more from the actual situation, resulting in the calculated results being more consistent with the experimental results. Through this paper, it is demonstrated that this linearly scaled molecular dynamics model can be used to study laser ablation results over tens of micrometers.

Modular Synthesis of Phosphino Hydrazones and Their Use as Ligands in a Palladium-Catalysed Cu-Free Sonogashira Cross-Coupling Reaction

Phosphino hydrazones represent a versatile class of nitrogen-containing phosphine ligands. Herein, we report a modular synthesis of phosphino hydrazone ligands by hydrazone condensation reaction of three different aryl hydrazines with 3-(diphenylphosphino)propanal (PCHO). Complexation reactions of these phosphino hydrazone ligands with palladium(II) and platinum(II) were investigated and the catalytic activity of the palladium(II) complexes was explored in a Cu-free Sonogashira cross-coupling reaction achieving yields up to 96 %. Additionally it was shown that the catalytically active species is homogeneous.