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Deep learning of electrochemical CO<sub>2</sub> conversion literature reveals research trends and directions

Jiwoo Choi, Kihoon Bang, Suji Jang, Jaewoong Choi, Juanita Ordoñez, David Buttler, Anna M. Hiszpanski, T. Yong-Jin Han, Seok Su Sohn, Byungju Lee, Kwang‐Ryeol Lee, Sang Soo Han, Dong-Hun Kim

2023Journal of Materials Chemistry A17 citationsDOIOpen Access PDF

Abstract

Machine learning (ML)-based protocol for selecting highly relevant papers, extracting important experimental data, and analyzing research trends &amp; directions focusing on the field of CO 2 reduction reactions (CO 2 RRs).

Topics & Concepts

Protocol (science)Computer scienceField (mathematics)Data scienceArtificial intelligenceElectrochemistryReduction (mathematics)Machine learningChemistryMathematicsElectrodeMedicinePhysical chemistryAlternative medicinePure mathematicsGeometryPathologyCO2 Reduction Techniques and CatalystsMachine Learning in Materials ScienceIonic liquids properties and applications
Deep learning of electrochemical CO<sub>2</sub> conversion literature reveals research trends and directions | Litcius