2023
DOI: 10.1021/acsami.3c02821
|View full text |Cite
|
Sign up to set email alerts
|

Machine Learning-Driven Discovery of Key Descriptors for CO2Activation over Two-Dimensional Transition Metal Carbides and Nitrides

B. Moses Abraham,
Oriol Piqué,
Mohd Aamir Khan
et al.

Abstract: Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental yet revolutionary science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
4
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 17 publications
(4 citation statements)
references
References 66 publications
0
4
0
Order By: Relevance
“…90–92 Experimentally, this activation was obtained by light irradiation ( λ = 427 nm), which induced a charge transfer from the catalyst to the molecule, CO 2 chemisorption with the elongation of the C–O bond, bending of O–C–O angle, and finally, dissociation of CO 2 on the catalyst surface into CO and O species. 93,94…”
Section: Resultsmentioning
confidence: 99%
“…90–92 Experimentally, this activation was obtained by light irradiation ( λ = 427 nm), which induced a charge transfer from the catalyst to the molecule, CO 2 chemisorption with the elongation of the C–O bond, bending of O–C–O angle, and finally, dissociation of CO 2 on the catalyst surface into CO and O species. 93,94…”
Section: Resultsmentioning
confidence: 99%
“…In recent years, the rapid development of data mining technology has provided a new approach for accelerating the screening of new materials. The combination of machine learning (ML) with DFT calculations or experiments has led to a new wave of prediction of material properties such as the band gap, free energy, , stability, and piezoelectricity. , The application of machine learning in MXene data sets also could help accelerate the research process of MXenes’ properties. The quality of the ML models was generally evaluated by train-test split scores or cross-validation results. However, a high score on the testing set does not mean that the model has an excellent extrapolation ability.…”
Section: Introductionmentioning
confidence: 99%
“…28 In addition, since the standard potential for CO 2 reduction is similar to that of the hydrogenolysis reaction, the involvement of a competing hydrogenolysis reaction (HER) in the catalytic process of MXenes decreases the efficiency of catalytic reduction of CO 2 . 29 Therefore, in order to improve the adsorption and activation of CO 2 molecules, the selectivity of target products and the reduction efficiency during the electrocatalytic reduction process of MXenes, the functionalization and modification of the surface of MXenes to improve their CO 2 reduction performance have become a hot spot in current research.…”
Section: Introductionmentioning
confidence: 99%