A Smarter Way to Convert CO₂: Data-Driven Discovery of Single-Atom Catalysts

Data-driven CO2 conversion catalyst discovery

Carbon dioxide (CO₂) may be a major climate concern, but it's also a potential feedstock for valuable chemicals—if only we can find the right catalysts. In a recent breakthrough, researchers at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) have developed a data-driven framework for identifying high-performance single-atom catalysts (SACs) for CO₂ electroreduction under realistic conditions.

Published in the Journal of Chemical Physics, this research represents a major advance in computational materials science by accounting for both pH effects and interfacial electric fields—two often-overlooked but critical variables in electrochemical catalysis.

Why CO₂ Reduction Matters

Electrochemical CO₂ reduction (CO₂RR) is a promising strategy for creating fuels and chemical feedstocks while reducing greenhouse gas emissions. A key target product is carbon monoxide (CO), which serves as a precursor for fuels and industrial chemicals. However, current catalysts struggle with inconsistent performance, especially under varying pH levels that affect intermediate binding.

A Unified Theoretical Model for Real-World Conditions

To solve this, the Tohoku team—led by Prof. Hao Li—developed a microkinetic model on the reversible hydrogen electrode (RHE) scale, incorporating both pH-dependent and field-induced effects on reaction intermediates. The model was supported by:

  • Spin-polarized DFT-D3 calculations
  • Data-driven screening of 101 d-block metal SACs
  • Evaluation of key intermediates like ∗COOH and ∗CO

This approach enabled precise predictions of CO₂RR activity across diverse SAC configurations such as pyrrole-, pyridine-, porphyrin-, and phthalocyanine-functionalized systems on graphene and COFs (covalent organic frameworks).

Key Findings: Selectivity and Stability

Among the 101 screened SACs, the model identified 12 top candidates—based on Fe, Cu, and Ni centers—exhibiting excellent CO selectivity across a broad pH spectrum. One of the model's novel insights was the role of dipole-field interactions in tuning adsorption energy, helping explain why certain catalysts excel under specific pH environments.

Model predictions showed strong agreement with experimental turnover frequency data, validating its utility in guiding real-world catalyst development.

Accelerating Innovation Through Open Science

All computational structures and data from the study have been made available through the open-access Digital Catalysis Platform, supporting global research collaboration. The team now plans to refine the model further, integrating machine learning algorithms to expand predictive capabilities and reduce simulation costs.

“This work bridges fundamental theory with application-driven catalyst screening,” said Prof. Li. “It moves us closer to deploying carbon-neutral electrochemical technologies at industrial scale.”

🔗 Original article citation: Phys.org – Data-driven approach identifies promising CO₂ conversion catalysts (May 21, 2025)

📘 Journal reference: The Journal of Chemical Physics – DOI: 10.1063/5.0267969

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