Choi, Jiwoo Bang, Kihoon Jang, Suji Choi, Jaewoong Ordonez, Juanita Buttler, David Hiszpanski, Anna Yong-Jin Han, T. Sohn, Seok Su Lee, Byungju Lee, Kwang-Ryeol Han, Sang Soo Kim, Donghun 2024-01-19T09:00:26Z 2024-01-19T09:00:26Z 2023-08-17 2023-09 2050-7488 https://pubs.kist.re.kr/handle/201004/113345 Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO2 reduction reactions (CO(2)RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO2RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO2RR catalysts, products, and performances, revealing the potentially effective material space in CO(2)RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO2RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions. English Royal Society of Chemistry Deep learning of electrochemical CO2 conversion literature reveals research trends and directions Article 10.1039/d3ta02780e 1 Journal of Materials Chemistry A, v.11, no.33, pp.17628 - 17643 Journal of Materials Chemistry A 11 33 17628 17643 N scie scopus 001033783100001 Chemistry, Physical Energy & Fuels Materials Science, Multidisciplinary Chemistry Energy & Fuels Materials Science Article NAMED ENTITY RECOGNITION CARBON-DIOXIDE REDUCTION SELECTIVITY CATALYSTS FORMATE ALLOY METAL ELECTROREDUCTION ELECTRODES