Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO₂ Reduction

Abstract

Electrocatalysis takes a significant role in the production of sustainable fuels and chemicals. The combination of artificial intelligence and catalytic science is exhibiting great potential to extract, analyze, and predict electrocatalysts. However, the currently developed machine learning approach usually requires a mass of data from density functional theory calculations to train and optimize models. In contrast, a knowledge graph has the potential to extract useful information from a large amount of the literature without referring to density functional theory. Herein, a knowledge graph of Cu-based electrocatalysts for electrocatalytic CO 2 reduction is constructed based on a linguistically enriched SciBERT-based framework. This framework retrieves multiple types of entities including material, regulation method, product, Faradaic efficiency, etc. from 757 scientific literature, generates representations with abundant domain-specific semantic information, and exhibits the capability to deal with electrocatalysts for CO 2 reduction. The obtained graph shows the development history of related catalysts, builds relationships between the factors associated with catalysis, and provides intuitive charts for researchers to gain useful information. Furthermore, we propose a deep learning-based prediction model, which integrates the semantic information from the scientific literature (word embedding) with the correlation of knowledge triples (graph embedding) and realizes the prediction of the Faradaic efficiency for a targeted case. This work paves the way for catalyst design in the manner of merging artificial intelligence with catalytic science.

CO2 Reduction beyond Copper-Based Catalysts: A Natural Language Processing Review from the Scientific Literature

“…This rigorous evaluation helped to ensure the accuracy and effectiveness of our strategy. It is worth noting that similar approaches have been attempted recently in the field of the CO 2 RR, but with a narrower focus on Cu-based catalysts for CO 2 ER and relying on only a few articles , or using these NLP tools in a general fashion . Unlike these previous analyses, our approach considers a larger corpus and has a broader scope: CO 2 catalysts beyond Cu-based materials.…”

Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures

“…CO 2 electroreduction has garnered significant attention from both the academic and industrial communities, owing to its potential to effectively mitigate greenhouse gas emissions while simultaneously producing fuels and chemicals 1 – 3 . Its widespread adoption relies heavily on the development of efficient and reliable electrocatalysts.…”

Insight into the intrinsic activity of various transition metal sulfides for efficient hydrogen evolution reaction

“…Among them, various transition metal sulfides, phosphates, nitrides, carbides, oxides, and hydroxides have been explored for their potential as HER electrocatalysts. 6–13 Specifically, the unique structural attributes, abundant active sites, and adjustable electronic properties of transition metal sulfides (TMSs) have garnered extensive research interest. These TMS materials typically manifest as layered MS 2 structures, each layer measuring approximately 0.6–0.7 nm in thickness, composed of a hexagonal layer of transition metal atoms sandwiched between two layers of sulfur (S) atoms.…”