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Machine-learning assisted modeling of single-atom catalysts for CO₂ hydrogenation to methanol

Company: Univ. Lorraine CNRS

Location: Nancy, London

Posted: June 07, 2026

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Context

The catalytic conversion of CO₂ into methanol is a central route for turning an abundant carbon source into a liquid energy carrier and chemical intermediate. At the atomic scale, this reaction depends on a delicate balance between CO₂ activation, hydrogen transfer, stabilization of C1 oxygenated intermediates and desorption of methanol or competing products. Small changes in the local geometry or electronic structure of the active site can therefore redirect the reaction pathway, which makes surface-level understanding essential for rational catalyst design.

Single-atom catalysts supported by graphene or N-doped graphene offer a controlled platform for this problem. An isolated transition-metal atom coordinated in a carbon/nitrogen environment can provide a well-defined active site while maximizing metal efficiency. However, the relevant chemical space remains large: the metal identity, coordination motif, nearby d...