Spatial arrangement of dynamic surface species from solid-state NMR and machine learning-accelerated MD simulations

Area of Science:

• Surface Science
• Catalysis
• Computational Chemistry

Background:

• Understanding the surface arrangement of mobile organic functionalities is crucial for designing efficient heterogeneous catalysts.
• Dynamic surface species play a significant role in cooperative catalytic processes.

Purpose of the Study:

• To explore the surface arrangement of motional organic functionalities.
• To correlate the distance between dynamic surface species with cooperative heterogeneous catalysis.

Main Methods:

• Experimental dipolar coupling measurements were used to probe surface arrangements.
• Molecular dynamics simulations predicted motionally-averaged coupling constants.
• Machine learning potentials were employed to achieve the necessary simulation timescales.

Main Results:

• The study successfully mapped the surface arrangement of motional organic functionalities.
• A correlation was established between the distance of dynamic surface species and catalytic cooperativity.
• The efficacy of machine learning potentials in simulating long timescales was demonstrated.

Conclusions:

• The spatial arrangement of mobile organic groups on catalyst surfaces is a critical factor in cooperative heterogeneous catalysis.
• Advanced computational methods, including machine learning potentials, are essential for studying dynamic surface phenomena.