High-index Facet Nanocatalysts for Clean Energy Applications

NU2023-109

INVENTORS

• Chad Mirkin*
• Bo Shen
• Zihao Ye

SHORT DESCRIPTION
A machine learning-guided, high-throughput DFT approach to synthesize high-index facet nanocatalysts with enhanced catalytic performance and broader material applicability.

BACKGROUND
Nanocrystals with high-index facets offer superior catalytic activity due to their abundance of reactive surface features. However, their synthesis remains challenging due to thermodynamic instability and limitations in current methods, which are often low-throughput and restricted to a narrow range of materials. Solid-state alloying/dealloying has shown promise but is constrained to specific metals and shapes. There is a critical need for a scalable, predictive approach to expand the chemical space and enable the rational design of high-performance nanocatalysts.

ABSTRACT
This technology leverages high-throughput density functional theory (DFT) calculations and machine learning to predict and guide the synthesis of high-index facet nanocrystals, particularly tetrahexahedral (THH) and hexoctahedral (HOH) shapes. By analyzing 117 host-guest metal combinations, the method identifies key features—Mendeleev number and atomic size differences—that influence facet formation. Experimental validation confirmed the formation of THH structures in multiple new metal systems, including Cu, Au, and Ag. These nanocatalysts demonstrated enhanced performance in CO₂ reduction reactions and exhibited excellent structural stability. The approach significantly accelerates discovery and expands the range of viable catalytic materials.

APPLICATIONS

• Electrocatalysts for CO₂ reduction: Converts CO₂ into fuels and chemical feedstocks.
• Fuel cell catalysts: Enhances oxygen reduction reactions for energy conversion.
• Ammonia synthesis: Catalyzes nitrogen or nitrogen oxide reduction.
• Water-splitting and hydrogen peroxide production: Supports clean energy and industrial oxidation processes.

ADVANTAGES

• Accelerated discovery: High-throughput DFT and machine learning streamline material identification.
• Expanded material scope: Enables synthesis beyond Pt-, Pd-, and Rh-based systems.
• Enhanced catalytic performance: High-index facets improve activity and selectivity.
• Validated predictive model: Over 80% accuracy in experimental confirmations.

IP STATUS

Patent Pending